Exoskeleton Robots for Rehabilitation and Healthcare Devices [1st ed.] 9789811547317, 9789811547324

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SPRINGER BRIEFS IN APPLIED SCIENCES AND TECHNOLOGY

Manuel Cardona Vijender Kumar Solanki Cecilia E. García Cena

Exoskeleton Robots for Rehabilitation and Healthcare Devices 123

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Manuel Cardona Vijender Kumar Solanki Cecilia E. García Cena •

Exoskeleton Robots for Rehabilitation and Healthcare Devices

123

•

Manuel Cardona Universidad Don Bosco Soyapango, El Salvador

Vijender Kumar Solanki CMR Institute of Technology Hyderabad, Telangana, India

Cecilia E. García Cena Universidad Politécnica de Madrid Madrid, Spain

ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISBN 978-981-15-4731-7 ISBN 978-981-15-4732-4 (eBook) https://doi.org/10.1007/978-981-15-4732-4 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020, corrected publication 2020 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore

Contents

1 Robotics for Rehabilitation: A State of the Art . . . . . . . . . Manuel Cardona, Marie Destarac, and Cecilia García Cena 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Upper Limb Exoskeleton Rehabilitation Robots . . . . . . 1.2.1 Lower Limb Exoskeleton Rehabilitation Robots 1.3 Future Trends . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2 Sensors and Motion Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Munish Jindal, Payal Garg, and Kiran Sood 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 Sensors Are Becoming an Inherent Part of Our Routine Life . . 2.2.1 Use of Sensors in Healthcare and Wellness Sector . . . . . 2.2.2 Sensors Are Getting Deeply Embedded with Technology in Every Sphere of Life Making Life Much Easier and Better . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.3 Sensor Technology and Motion Systems in Vehicles . . . 2.2.4 Sensor Technology in Home and Garden . . . . . . . . . . . 2.3 Sensors Are Changing the Vision of the Health Care Sector as Smart Sensors Are Initiating Will Appropriate Response, Accurate Analysis Along with Advising the Right Line of Treatment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Motion-Sensor Technology Facilitating Elderly Patient Monitoring as Well as Specially Abled to Move Freely . 2.3.2 Medical Delivery Robots in Hospitals . . . . . . . . . . . . . . 2.3.3 Role of Motion Sensors or Mobility Aids in Medical Field . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Types of Mobility Aids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.1 Walkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.2 Rollators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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2.4.3 Wheelchairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.4 Electric Wheelchairs Scooter . . . . . . . . . . . . . . . . . . . . 2.4.5 KartBot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4.6 People Getting Benefitted from Mobility Aids . . . . . . . . 2.5 Hoverboards as an E-Mobility Device Used for Commuting, Maneuvering, Transportation, for Quick First Aid and Delivery of Medical Supplies, Carrying Patients and Most Importantly as Portable Wheelchairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5.1 Hoverboards Are the Futuristic E-Mobility Devices; Precisely We Can Say Future of Mobility, Decoding Future for Mankind . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Gait Capture Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Manuel Cardona, José Yúdice, Francisco Huguet, Gabriel López, Cecilia E. García Cena, and Vijender K. Solanki 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Motion Capturing Systems . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Vicon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Motion Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Optitrack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Qualisys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Codamotion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 The Proposed System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Data Acquisition Software . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Development Platform . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Software Features . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 Sensor Data Reception and Processing . . . . . . . . . . 3.4.4 Data Deployment and .txt File Generation . . . . . . . 3.5 Testing and Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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4 Technologies for Therapy and Assistance of Lower Limb Disabilities: Sit to Stand and Walking . . . . . . . . . . . . . . . . . . . . Isela Carrera, Hector A. Moreno, Sergio Sierra, Alexandre Campos, Marcela Munera, and Carlos A. Cifuentes 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Conditions Affecting Mobility . . . . . . . . . . . . . . . . . . . . . . . . 4.2.1 Cerebrovascular Accident or Stroke . . . . . . . . . . . . . . 4.2.2 Spinal Cord Injury (SCI) . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Cerebral Palsy (CP) . . . . . . . . . . . . . . . . . . . . . . . . . .

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4.2.4 Elderly . . . . . . . . . . . . . . . . . . . . . . . 4.2.5 Other Conditions . . . . . . . . . . . . . . . . 4.3 Biomechanics of the STS Task and Walking . 4.3.1 Biomechanics of the Sit to Stand Task 4.3.2 Biomechanics of Walking . . . . . . . . . 4.4 Traditional Rehabilitation Therapy . . . . . . . . . 4.4.1 Sit to Stand and Balance Therapies . . . 4.4.2 Gait Therapy . . . . . . . . . . . . . . . . . . . 4.5 Sit to Stand Assistance and Therapy Robots . . 4.6 Gait Assistance and Therapy Robots . . . . . . . 4.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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5 Adaptable Robotic Platform for Gait Rehabilitation and Assistance: Design Concepts and Applications . . . . . . . . . . . . . . . Sergio Sierra, Luis Arciniegas, Felipe Ballen-Moreno, Daniel Gomez-Vargas, Marcela Munera, and Carlos A. Cifuentes 5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Biomechatronic Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Targeted Activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Human Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.3 Mechanical Structure and Actuators . . . . . . . . . . . . . . . 5.2.4 Physical Interfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Physical Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Sensor Interfaces for Gait Phase Detection and Physical Interaction in a Lower Limb Exoskeleton . . . . . . . . . . . . . . . . . 5.3.1 Gait Phase Detection . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Sensors for Physical Interaction . . . . . . . . . . . . . . . . . . 5.4 Variable-Impedance Controllers for Hip–Knee Robotic Exoskeletons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Actuation Systems and Control for Ankle Rehabilitation . . . . . . 5.5.1 Actuation Systems Implemented on AAFOs . . . . . . . . . 5.5.2 Control Strategies for AAFOs . . . . . . . . . . . . . . . . . . . 5.6 Smart Walkers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7 Applications of the AGoRA Project in Therapy and Daily Life Assistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.1 Sit to Stand Activity with Lower Limb Exoskeleton and Smart Walker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.7.2 Walking a Ramp Using a Smart Walker . . . . . . . . . . . . 5.7.3 Stationary Therapy for Treatment of Spasticity with the Ankle Exoskeleton T-FLEX . . . . . . . . . . . . . . . . . . . . . 5.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Correction to: Technologies for Therapy and Assistance of Lower Limb Disabilities: Sit to Stand and Walking . . . . . . . . . . . . . . . . . . . . . Isela Carrera, Hector A. Moreno, Sergio Sierra, Alexandre Campos, Marcela Munera, and Carlos A. Cifuentes

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About the Authors

Manuel Cardona Ph.D. c received the B.S. degree in Electrical Engineering from Universidad de Sonsonate (USO), El Salvador, in 2004 and the M.Sc. degree in Automation and Robotics from Universidad Politécnica de Madrid, Madrid, Spain, in 2008. From 2007 to 2008, and 2011, he was a Research Assistant with the Robotics and Intelligence Machines Research Group at Universidad Politécnica de Madrid, Spain. He has a Postgraduate Degree in Scientific Research and a Postgraduate Degree and Innovation Management. He is currently a Ph.D. candidate in Automation and Robotics at Universidad Politécnica de Madrid. Since 2014, he has been a professor and the director of the Robotics and Intelligence Machines research group and Computer Vision research group, School of Engineering at Universidad Don Bosco (UDB), El Salvador. His research interest includes Rehabilitation Robotics, Biomechanics, kinematic and dynamic of serial and parallel Robots, embedded systems, vision and artificial intelligence, and applications of robotics systems. He has authored or co-authored more than 30 research articles that are published in various journals, books and conference proceedings. He is an Associate Editor in International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) ISSN 2581-3242. He is an IEEE Senior Member, He belongs to Robotics and Automation Society (RAS), Aerospace and Electronic Systems Society (AESS) and Education Society (EdSOC). He is IEEE RAS Student Branch Chapter Advisor and Student Brach Mentor at Universidad Don Bosco, and the Vicechair at IEEE El Salvador Section. Vijender Kumar Solanki Ph.D. is an Associate Professor in Computer Science & Engineering, CMR Institute of Technology (Autonomous), Hyderabad, TS, India. He has more than 10 years of academic experience in network security, IoT, Big Data, Smart City and IT. Prior to his current role, he was associated with Apeejay Institute of Technology, Greater Noida, UP, KSRCE (Autonomous) Institution, Tamilnadu, India and Institute of Technology & Science, Ghaziabad, UP, India. He is member of ACM and IEEE. ix

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He has attended an orientation program at UGC-Academic Staff College, University of Kerala, Thiruvananthapuram, Kerala & Refresher course at Indian Institute of Information Technology, Allahabad, UP, India. He has authored or co-authored more than 50 research articles that are published in various journals, books and conference proceedings. He has edited or co-edited 14 books and Conference Proceedings in the area of soft computing. He received Ph.D in Computer Science and Engineering from Anna University, Chennai, India in 2017 and ME, MCA from Maharishi Dayanand University, Rohtak, Haryana, India in 2007 and 2004, respectively and a bachelor’s degree in Science from JLN Government College, Faridabad Haryana, India in 2001. He is the Book Series Editor of Internet of Everything (IoE): Security and Privacy Paradigm, CRC Press, Taylor & Francis Group, USA; Artificial Intelligence (AI): Elementary to Advanced Practices Series, CRC Press, Taylor & Francis Group, USA; IT, Management & Operations Research Practices, CRC Press, Taylor & Francis Group, USA; Bio-Medical Engineering: Techniques and Applications with Apple Academic Press, USA and Computational Intelligence and Management Science Paradigm, (Focus Series) CRC Press, Taylor & Francis Group, USA. He is Editor-in-Chief in International Journal of Machine Learning and Networked Collaborative Engineering (IJMLNCE) ISSN 2581-3242; International Journal of Hyperconnectivity and the Internet of Things (IJHIoT), ISSN 2473-4365, IGI-Global, USA, Co-Editor Ingenieria Solidaria Journal ISSN (2357-6014), Associate Editor in International Journal of Information Retrieval Research (IJIRR), IGI-GLOBAL, USA, ISSN: 2155-6377 | E-ISSN: 2155-6385. He has been guest editor with IGI-Global, USA, InderScience & Many more publishers. He can be contacted at [email protected]. Cecilia E. García Cena Ph.D. is Assistant Professor on Automation and Robotics with the Universidad Politécnica de Madrid (UPM), Madrid, Spain, and research member at the Centre for Automation and Robotics. She is Visiting Professor with Ecoles Universitaries Gimbernat, Cantabria, Spain and Beihang University, Pekin, China. Between 2004 and 2006, she was also Visiting Professor with Universidad Carlos III (UC3M), Madrid, Spain. She is leading the robotics medical applications in Robots and Intelligence Machine Research Group, Centre for Automation and Robotics (CAR). She has been the head director of national research and development projects, and research member of European projects. Since 2016, she is an expert evaluator for European Commission in Robotics topics. Since 2015 she is strongly involved in the development of medical devices with clinical and social applications. She is the author of over 60 international scientific publications. She holds four patents, two of them registered internationally.

Chapter 1

Robotics for Rehabilitation: A State of the Art Manuel Cardona , Marie Destarac , and Cecilia García Cena

1.1 Introduction An exoskeleton robot is an electromechanical device that can be used by a person to increase its physical abilities, assist in disability cases or assist in therapeutic rehabilitation cases. An exoskeleton includes a mechanical structure that could composed of active and passive joints, a power system (which are usually electric actuators), and sensors to measure the torque applied by the motors. In addition, some of them include sensors to capture biological signals, such as electromyography (EMG) to measure muscle signals or electroencephalography (EEG) that allow to capture brain electrical signals and convert them into command signals. In the case of exoskeletons for rehabilitation, these devices are intended to help patients in its rehabilitation after having suffered some type of muscle or nerve injury, or to exercise the elderly population. Rehabilitation is achieved through training routines previously established by a professional. According to the National Spinal Cord Injury Statistical Center (NSCISC), the spinal cord injury incidence is approximately 54 cases per million people in the United States, having approximately 17,500 new cases every year. In addition, the prevalence rate of people in 2017 was approximately 285,000 [1]. M. Cardona (B) · M. Destarac · C. G. Cena Centro de Automática y Robótica, Universidad Politécnica de Madrid, Madrid, Spain e-mail: [email protected] M. Destarac e-mail: [email protected] C. G. Cena e-mail: [email protected] M. Cardona Universidad Don Bosco (UDB), Soyapango, El Salvador © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 M. Cardona et al., Exoskeleton Robots for Rehabilitation and Healthcare Devices, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-15-4732-4_1

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The use of these devices for rehabilitation therapies becomes a very valuable tool for therapists, since it assists them during the sessions. In addition, it has been demonstrated through different studies [2–6], that the use of exoskeletons in rehabilitation processes generates positive results. In fact, in the last 15 years, research in the field of rehabilitation robotics has grown exponentially as shown in [7–9]. This is due to several factors since these devices provide assistance to physiotherapists to perform the repetitive movements that the patient must do, thus reducing the physical overload of the professional [10]. In addition, different causes such as sedentary lifestyle, population aging and low birth rates, especially in developed countries, provide for a worrying future about the capacity of public healthcare systems in the field of rehabilitation. The clinical use of these devices can reduce the waiting lists in rehabilitation services by being able to attend several patients at the same time, according to the number of available exoskeletons.

1.2 Upper Limb Exoskeleton Rehabilitation Robots According to data published by the World Health Organization (WHO), currently 1.7 billion people worldwide suffer a musculoskeletal injury [11]. Among them, 20% are directly related to the shoulder and its causes are diverse such as falls, accidents at work, traffic accidents, stroke, among others. Many of the rehabilitation exoskeletons have emerged with the aim of helping in neuro-rehabilitation in patients who have suffered a stroke, the main cause of disability in adults [12]. However, there are other conditions that can also affect the mobility of the upper limb and they can treat it with a rehabilitation exoskeleton, such as the musculoskeletal injuries [13] or multiple sclerosis [14]. Several examples of exoskeletons for upper limbs can be found in [12, 15–20]. The devices that are currently on the market and have a TRL 9 level are the ArmeoPower, the InMotion ARM, the KINARM Lab and the ReoGo. The ALEx and ORTE exoskeletons are still pending approval to be marketed, however, their degree of development or TRL (7/8) already allows them to be differentiated from prototypes that are still under investigation. “Armeo Therapy Concept” from the Swiss company Hocoma, consists of three devices that are used in different phases of the patient’s rehabilitation process. The ArmeoPower is the only one that has actuators and therefore can be used to treat different pathologies in which the functionality of the arm is limited, such as stroke, orthopedic or neurological disorders, injuries, etc [21]. ArmeoPower is shown in Fig. 1.1(a), it has 6 active degrees of freedom (DoF) covering 85% of the 3-dimensional (3D) workspace of a healthy arm, allowing the following movements: abduction/adduction, flexion/extension and internal/external rotation of the shoulder, as well as flexion/extension of the elbow and wrist, and pronation/supination of the forearm. Studies of the use of this device have been performed in patients with stroke [22] and multiple sclerosis [23], reporting positive

1 Robotics for Rehabilitation: A State of the Art

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(d) ReoGo

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Fig. 1.1 Main Upper Limb Exoskeleton Robots Companies [25]. a Hocoma, b Bionik, c BKIN Technologies, d Motorika, Medical, e Wereable Robotics, f AURA Robotics

results in them. Other studies that are currently being carried out are presented in [24]. One of the first exoskeletons created for upper limb rehabilitation is the InMotion ARM, developed at the Massachusetts Institute of Technology (MIT) under the name of MIT-Manus, and currently marketed by Bionik [15]. The rehabilitation that can be performed with this device, shown in Fig. 1.1(b), is in 2D and the active movements it covers are the internal/external rotation of the shoulder and the flexion/extension of the elbow . It has a passive movement to rehabilitate, which is the protractionretraction of the shoulder. The pathologies that can be treated with the InMotion ARM are Parkinson’s, stroke, cerebral palsy, multiple sclerosis, among others. The Kinarm Lab is a 2D rehabilitation device that can be used by children and adults, Fig. 1.1(c). The developer offers the option to rent or buy it for research purposes. Among its main features is the capacity to obtain accurate data on the impact of a brain injury or disease on the motor, sensory and cognitive functions of the patient [26, 27]. The ReoGo device, Fig. 1.1(d), was developed to treat different neurological conditions, as well as dysfunctions due to orthopedic problems or post-surgery. It has 2 active and one passive DoF that allow 3D rehabilitation and horizontal abduction/adduction movements of the shoulder, flexion/extension of the elbow and shoul-

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der. Some studies have been performed on patients who have suffered a stroke, as well as analysis of the functionality of the device [2, 28, 29]. The Arm Lightweight Exoskeleton (ALEX) was developed by Wereable Robotics company, a spin-off of Santa Ana High School in Pisa, Italy. They develop and produce robotic systems of rehabilitation to assistance and increase the human strength. Kinetek is the medical division of the company and is responsible for the development of ALEX. ALEX has 6 DoF in total, 4 of them are active and 2 are passives. The motion includes: flexo-extension, abduction/adduction and internal/external rotation of the shoulder, and flexion-extension of the elbow, which are activated and sensed. The passive DoF are the prono-supination of the forearm and the flexo-extension of the wrist [3, 30]. One of the peculiarities of ALEX is the rotation mechanism of the shoulder, which was patented by the company, and which allows the human arm to be aligned with the joints axes. It is equipped with four controlled brushless motors on the back of the device, four incremental optical encoders located on the motor shafts and absolute angular position sensors. For torque transmission, a system of cables from the motors to the joints is used. The device has two versions that are for unilateral or bilateral therapy, and in Fig. 1.1e this last modality is shown. Finally, ORTE is a shoulder and elbow rehabilitation exoskeleton and has 6 DoF that allows abduction/adduction, flexion/extension and internal/external rotation of the shoulder, as well as elbow flexion/extension, pronation/supination of the forearm and elevation/depression of the scapula [31]. In addition, ORTE has a musculoskeletal simulator of the upper limb that helps to assess the patient’s muscular state and establish personalized therapies [32]. Table 1.1 summarizes the main features of the upper limb rehabilitation robots.

1.2.1 Lower Limb Exoskeleton Rehabilitation Robots There is a limited number of lower limb exoskeletons available in the market. Among them are: Lokomat, ReWalk, HAL, Ekso GT, Rex and Indego (Fig. 1.2). Lokomat was the first exoskeleton available in the market, includes a treadmill and has 2 active DoF for each limb (Flexion/extension of the hip and knee), has CE marking and is pending the FDA approval (Food and Drug Administration). Studies have been conducted on the effectiveness of the use of Lokomat as a rehabilitation device in patients with stroke [4, 33], spinal cord injuries [34, 35], and cerebral palsy [36, 37]. ReWalk was the first FDA-approved rehabilitation exoskeleton for personal use. It has 2 active DoF per leg (Flexion/extension of the hip and knee). The user can control the movements by changing their center of gravity, which is achieved by tilting. For example, if the user leans forward the system captures the inclination and starts the first step facilitating the patient’s progress. In addition, it is the exoskeleton that has greater battery autonomy with 8 h of continuous use. Rewalk has been tested

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Table 1.1 Main features of Upper Limb Rehabilitation Robots Product Company DoF Pathologies ArmeoPower

Hocoma

InMotion ARM Bionik

6 active

2 active, 1 passive

KINARM lab

BKINtTechnologies 2 passive

ReoGo

Motorika medical

2 active, 1 passive

ALEx

Wereable robotics

4 active, 2 passive

ORTE

Aura innovative robotics

6 active

Stroke, multiple sclerosis, cerebral palsy, Parkinson, neuropathies, neurological or orthopedic disorders, musculoskeletal or cerebral lesions Stroke, Parkinson, multiple sclerosis, cerebral palsy and spinal cord injury Stroke, autism, paralysis and brain injuries Stroke, spinal cord injury, neurological disorders and musculoskeletal injuries Stroke, neurological or orthopedic disorders and musculoskeletal injuries Stroke, multiple sclerosis, neurological or orthopedic disorders and musculoskeletal injuries

Approval TRL CE, FDA 9

CE, FDA 9

Pending

9

CE, FDA 9

Pending

8/9

Pending

7

in patients with spinal cord injuries [38–40] and with paraplegia [41, 42], providing positive results. The HAL (Hybrid Assistive Limb) exoskeleton is the only commercial exoskeleton to date to use control signals from EMG sensors allowing the detection of patient movement intensity, has CE marking and is in the process of FDA approval. HAL has 2 DoF per leg (Flexion/extension of the hip and knee) and helps in the rehabilitation of people with brain, spinal or neuromuscular injuries. Studies of the use of HAL have been performed in patients with spinal cord injuries [5, 6], and ictus [43–45], reporting good results. Ekso GT is an exoskeleton for the rehabilitation of people with gait disorders. Positive results have been reported from trials with patients with stroke [46, 47] and spinal cord injuries [48, 49]. It has CE marking and FDA approval. The device has 2 active DoF (Flexion/extension of the hip and knee) and 1 passive (Plantar/Dorsal

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(a) Lokomat

(d) Ekso GT

(b) ReWalk

(e) Rex

(c) HAL

(f) Indego

Fig. 1.2 Main Lower Limb Exoskeleton robots Companies. a Hocoma, b ReWalk Robotics, c Cyberdyne, d Ekso Bionics, e Rex Bionics, f Indego (Parker Hannifin)

flexion of the ankle) for each leg, and detects the intention of walking when the patient swings on his pelvis. Rex is a mechanism of 5 active DoF for each leg (Flexion/extension and abduction/adduction of the hip, flexion/extension of the knee, plantar/dorsal flexion of the ankle and inversion/eversion of the foot) making a total of 10 linear actuators . Rex has CE marking and is the only one to date that does not need additional balancing devices, such as crutches or walker, since it incorporates a self-balancing system. Rex has the disadvantage of being very heavy which makes it quite slow. In the case of Rex, no conclusive results of studies validating the use of this device as a rehabilitation aid have been reported to date. Indego was designed to spinal cord injuries and gait disorders. It has CE marking and was the second exoskeleton to receive FDA approval for personal use. It has 2 DoF per leg (Flexion/extension of the hip and knee), it is the most modular and lightest on the market (12 kg) making it the most portable lower limb exoskeleton.

1 Robotics for Rehabilitation: A State of the Art Table 1.2 Main features of Lower Limb Rehabilitation Robots Product Company DoF Pathologies Lokomat

Hocoma

2 activos

ReWalk

ReWalk Robotics

2 active

HAL

Cyberdyne

2 activos

Ekso GT

Ekso bionics 2 active, 1 passive

Rex

Rex bionic

5 active

Indego

Indego

2 active

Stroke, Traumatic brain injuries, Paraplegia, Cerebral Palsy, Multiple Sclerosis, Parkinson’s, Endoprosthesis, Degenerative Diseases, Muscular Atrophy in the Column Stroke, paralysis, brain injuries, spinal cord injuries Stroke, cerebral palsy, nervous or muscular disorders, spinal cord injury Stroke, spinal cord injury, paralysis, gait disorders Stroke, Spinal Cord Injuries, Muscular Dystrophy, Multiple Sclerosis, Postpolio Syndrome Spinal cord injuries, gait rehabilitation

7

Approval

TRL

CE, FDA

9

CE, FDA

9

CE

9

CE, FDA

9

CE

9

CE, FDA

9

The company reports positive results for the rehabilitation of patients with stroke [50] and paraplegia [51–53]. A summary with the characteristics of each exoskeleton is shown in the Table 1.2.

1.3 Future Trends Exoskeleton robots have become a suitable tool to help in rehabilitation process. The objective is that they cover the intermediate stage of rehabilitation, in which repetitive movements are performed, leaving the initial and final stage in charge of the specialist. In addition, its use is also convenient as an instrument for measuring the evolution of the patient, by providing data to the physiotherapist to make the right decisions about the most appropriate exercises.

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Although the development of rehabilitation exoskeletons has gained boom in recent years, few commercial examples are found. This is due to the long and expensive process that requires compliance with the regulations that allow the sale and clinical use of the devices. On the one hand, companies must submit the equipment to safety tests that are destructive, and on the other, to clinical trials that support its effectiveness in the treatment of patients. According to the International Federation of Robotics IFR, sales of exoskeletons increased from 5,581 units in 2016 to 6,068 units in 2017. Although there is still no official data, it is believed that sales in 2018 were around 7,000 units, this would represent 15% more than in 2017. It is estimated that 40,500 units will be sold between 2019 and 2021, representing a compound annual growth rate (TCAC) of approximately 37%. Markets & Markets predicts that the exoskeleton market is expected to grow from USD 104.3 million in 2016 to USD 2,810.5 million by 2023, with a TCAC of 45.2% between 2017 and 2023. This growth is mainly due to factors such as the growing demand of the health sector in rehabilitation robotics, advances in robotic technologies and to large investments for the development of exoskeleton technology. On the other hand, it will be necessary to seek for the improvement in the actuation systems, not only more compact motors with less noise, smaller and more efficient, but also motors that provide the necessary torque at the required speeds, with a required low consumption, this leads to drive the design and construction of actuators specifically intended for use in exoskeletons instead of using general purpose motors as is currently done. Moreover, it is necessary to work on the development of new power supplies, not only to increase the autonomy of the mechanism but also to reduce the total weight of the exoskeleton. Another important challenge represents the improvement in stabilization systems, so that the device can be used without the need for crutches or walkers, this will bring an increase in comfort and independence. Finally, advancement in sensor technology is necessary, not only to reduce the weight of the devices with their miniaturization, but also to improve the detection of movement intentions. in a way that optimizes the control strategies and assistance that exoskeletons provide to users. Acknowledgements The authors would like to thanks to the Government of El Salvador for its support through the “Fondo de Investigación de Educación Superior (FIES)”. Manuel Cardona would like to thank to Fundación Carolina and Universidad Don Bosco for their support during this research.

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References 1. National Spinal Cord Injury Statistical Center, Facts and Figures at a Glance (University of Alabama at Birmingham, Birmingham, 2017) 2. K. Takahashi, K. Domen, T. Sakamoto et al., Efficacy of upper extremity robotic therapy in subacute poststroke hemiplegia: an exploratory randomized trial. Stroke 47(5), 1385–8 (2016) 3. E. Pirondini, M. Coscia, S. Marcheschi et al., Evaluation of the effects of the Arm Light Exoskeleton on movement execution and muscle activities: a pilot study on healthy subjects. J. Neuroeng. Rehabil. 13(9) (2016) 4. J. Hidler, D. Nichols, M. Pelliccio et al., Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke. Neurorehabil. Neural Repair 23(1), 5–13 (2008) 5. D. Grasmücke, A. Zieriacks, O. Jansen et al., Against the odds: what to expect in rehabilitation of chronic spinal cord injury with a neurologically controlled Hybrid Assistive Limb exoskeleton. A subgroup analysis of 55 patients according to age and lesion level. NeuroRehabil. J. (2017) 6. M. Sczesny-Kaiser, O. Höffken, M. Aach et al., HAL exoskeleton training improves walking parameters and normalizes cortical excitability in primary somatosensory cortex in spinal cord injury patients. J. Neuroeng. Rehabil. (2015) 7. F. Yakub, A.Z. Khudzari, Y. Mori, Recent trends for practical rehabilitation robotics, current challenges and the future. Int. J. Rehabil. Res. 37(1), 9–21 (2014) 8. L. Chong, S. Jianfeng, J. Linhong, Lower limb rehabilitation robots: a review, in World Congress on Medical Physics and Biomedical Engineering (2012), pp. 2042–2045 9. P. Maciejasz, J. Eschweiler, K. Gerlach-Hahn et.al., A survey on robotic devices for upper limb rehabilitation. J. Neuroeng. Rehabil. 11(3) (2014) 10. R. Riener, M. Guidali, U. Keller, A. Duschau-Wicke, V. Klamroth, T. Nef, Transferring ARMin to the Clinics and Industry. Top. Spinal Cord. Inj. Rehabil. 17(1), 54–59 (2011) 11. C. Murray et al., Global Burden of Diseases, Injuries and Risk Factors Study 2013. The Lancet (2014) 12. T. Proietti, V. Crocher, A. Roby-Brami et al., Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies. IEEE Rev. Biomed. Eng. 9, 4–14 (2016) 13. T. Desplenter, A. Kyrylova, T. Stanbury et al., A werable mechatronic for arm rehabilitation, in 5th IEEE International Conference on Biomedical Robotics Biomechanics (2014), pp. 491–496 14. I. Lamers, A. Maris, D. Severijns et al., Upper Limb Rehabilitation in People with Multiple Sclerosis: A Systematic Review. Neurorehabil. Neural Repair. 30(8), 773–793 (2016) 15. H. Shing, S. Quan, Exoskeleton robots for upper-limb rehabilitation: State of the art and future prospects. Med. Eng. Phys. 34(3), 261–268 (2012) 16. M. Babaiasl, S. Mahdioun, P. Jaryani, M. Yazdani, A review of technological and clinical aspects of robot-aided rehabilitation of upper-extremity after stroke. Disabil. Rehabil. Assist Technol. 11(4), 263–280 (2015) 17. R.A. Gopura, D.S. Bandara, K. Kiguchi, G.K. Mann, Developments in hardware systems of active upper-limb exoskeleton robots: A review. Rob. Auton. Syst. 75, 203–220 (2016) 18. B. Volpe, H. Krebs, N. Hogan et al., Robot training enhanced motor outcome in patients with stroke maintained over 3 years. Neurology 53(8), 1874–1876 (1999) 19. G. Prange, M. Jannink, C. Groothius et al., Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J. Rehabil. Res. Dev. 43(2), 171–184 (2006) 20. J. Mehrholz, T. Platz, J. Kugler et al., Electromechanical and robot assisted arm training for improving arm function and activities of daily living after stroke. Cochrane Database Syst. Rev. 8(4) (2008) 21. T. Nef, R. Riener, ARMin: design o novel arm rehabilitation robot, in 9th IEEE Conference on Rehabilitation Robotics (2005), pp.57–60 22. V. Klamroth-Marganska, J. Blanco, K. Campen et al., Three-dimensional, task-specific robot therapy of the arm after stroke: a multicentre, parallel-group randomised trial. Lancet Neurol. 13(2), 159–166 (2014)

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23. R. Calabro, M. Russo, A. Naro et al., Robotic gait training in multiple sclerosis rehabilitation: can virtual reality make the difference? Findings from a randomized controlled trial. J. Neurol. Sci. 377, 25–30 (2017) 24. Hocoma, Home - Hocoma (2017), https://www.hocoma.com/. Accessed 26 June 2017 25. M. Cardona, M.A. Destarac, C.E. García, Exoskeleton robots for rehabilitation: State ofthe art and future trends, IEEE 37th Central America and Panama Convention (CONCAPAN XXXVII). Managua 2017, 1–6 (2017).https://doi.org/10.1109/CONCAPAN.2017.8278480 26. S. Scott, Apparatus for measuring and perturbing shoulder and elbow joint positions and torques during reaching. J. Neurosci. Methods 89, 119–127 (1999) 27. S. Ball, I. Brown, S. Scott, MEDARM: a rehabilitation robot with 5DOF at the shoulder complex, in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2007), pp. 4–7 28. I. Treger, S. Faran, H. Ring, Robot-assisted therapy for neuromuscular training of sub-acute stroke patients. A feasibility study. Eur. J. Phys. Rehabil. Med. 44(4), 431–5 (2008) 29. F. Bovolenta, P. Sale, V. Dall’Armi, P. Clerici, M. Franceschini, Robot-aided therapy for upper limbs in patients with stroke-related lesions. Brief report of a clinical experience. J. Neuroeng. Rehabil. 8 (2011) 30. E. Ruffaldi, M. Barsotti, D. Leonardis et al., Evaluating virtul embodiment with the ALEx exoskeleton. Haptics: Neurosci. Device Model. Appl. 8618, 133–140 (2014) 31. J. García, C.E. García, L. Monge et al., in Mechanical Design of a Robotic Exoskeleton for Upper Limb Rehabilitation, Advances in Automation and Robotics Research in Latin America, ed. by I. Chang, et al. (Springer International Publishing, Berlin, 2017), pp. 297–308 32. M.A. Destarac, C.E. García, R. Saltarén, et al., Modeling and simulation of upper brachialplexus injury. IEEE Syst. J. 10(3), 912–921 (2016) 33. K. van Kammen, A. Boonstra, L. van der Woude et al., Differences in muscle activity and temporal step parameters between Lokomat guided walking and treadmill walking in poststroke hemiparetic patients and healthy walkers. Neurorehabil. Neural Repair 23(1), 5–13 (2008) 34. A. Domingo, T. Lam, Reliability and validity of using the Lokomat to assess lower limb joint position sense in people with incomplete spinal cord injury. J. Neuroeng. Rehabil. (2014) 35. A.E. Chisholm, R.A. Alamro, A.M. Williams, T. Lam, Robot-assisted gait training (Lokomat) improves walking function and activity in people with spinal cord injury: a systematic review. J. Neuroeng. Rehabil. (2017) 36. T. Aurich-Schuler, B. Warken, J.V. Graser et al., Practical recommendations for robot-assisted treadmill therapy (Lokomat) in children with cerebral palsy: indications, goal setting, and clinical implementation within the WHO-ICF framework. Neuropediatrics (2015) 37. A. AKoenig, M. Wellner, S. Köneke et al., Virtual gait training for children with cerebral palsy using the Lokomat gait orthosis. Stud Health Technol. Inf. (2008) 38. K. Raab, K. Krakow, F. Tripp, M. Jung, Effects of training with the ReWalk exoskeleton on quality of life in incomplete spinal cord injury: a single case study. Spinal Cord Ser Cases (2016) 39. G. Zeilig, H. Weigarden, M. Zwecker et al., Safety and tolerance of the ReWalkRM exoskeleton suit for ambulation by people with complete spinal cord injury: a pilot study. J. Spinal Cord Med. 35, (2012) 40. T. Platz, A. Gillner, N. Borgwaldt, S. Kroll, S. Roschka, Device-training for individuals with thoracic and lumbar spinal cord injury using a powered exoskeleton for technically assisted mobility: achievements and user satisfaction. Biomed. Res. Int. (2016) 41. P. Asselin, S. Knezevic et al., Heart rate and oxygen demand of powered exoskeleton-assisted walking in persons with paraplegia. J Rehabil Res Dev. (2015) 42. D.B. Fineberg, P. Asselin, N.Y. Harel et al., Vertical ground reaction force-based analysis of powered exoskeleton-assisted walking in persons with motor-complete paraplegia. J. Spinal Cord. Med. 313–321 (2013) 43. H. Watanabe, R. Goto, N. Tanaka et al., Effects of gait training using the Hybrid Assistive Limb® in recovery-phase stroke patients: A 2-month follow-up, randomized, controlled study. NeuroRehabil. J. 363–367 (2017)

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44. R. Kasai, S. Takeda, The effect of a Hybrid Assistive Limb on sit-to-stand and standing patterns of stroke patients. J. Phys. Ther. Sci. (2016) 45. T. Yoshimoto, I. Shimizu, Y. Hiroi, Sustained effects of once-a-week gait training with hybrid assistive limb for rehabilitation in chronic stroke: case study. J. Phys. Ther. Sci. (2016) 46. A. Russo, K. Endersby, M. Perret et al., A robotic exoskeleton to provide increased mass practice for gait training and its impact on discharge destination for individuals with acute stroke (Poster Presentation, ISC Meeting, 2016) 47. K. Hohl, S.L. Deems-Dluhy, A. Jayaraman, K. Scanlan, Exoskeleton gait training for individuals affected by severe, chronic stroke (Platform Presentation, ACRM Meeting, 2016) 48. A.J. Kozlowski, T.N. Bryce, M.P. Dijkers, Time and effort required by persons with spinal cord injury to learn to use a powered exoskeleton for assisted walking. Top Spinal Cord Inj. Rehabil. (2015) 49. S.A. Kolakowsky-Hayner et al., Safety and feasibility of using the EksoTM bionic exoskeleton to aid ambulation after spinal cord injury. J. Spine 4 (2013) 50. A. Spencer, H. Kevin, G. Michael, An Assistive Controller for a Lower-Limb Exoskeleton for Rehabilitation after Stroke, and Preliminary Assessment Thereof. Conf. Proc. IEEE Eng. Med. Biol. Soc. (2015) 51. A. Ekelem, S. Murray S, M. Goldfarb, Preliminary assessment of variable geometry stair ascent and descent with a powered lower limb orthosis for individuals with paraplegia. Conf. Proc. IEEE Eng. Med. Biol. Soc. (2015) 52. R.J. Farris, H.A. Quintero, M. Goldfarb, Performance evaluation of a lower limb exoskeleton for stair ascent and descent with paraplegia. Conf Proc IEEE Eng Med Biol Soc (2012) 53. H. Quintero, R. Farris, C. Hartigan et al., A Powered Lower Limb Orthosis for Providing Legged Mobility in Paraplegic Individuals. Top Spinal Cord Inj Rehabil (2011)

Chapter 2

Sensors and Motion Systems Munish Jindal, Payal Garg, and Kiran Sood

2.1 Introduction A motion system with a sensor called a motion detector is a system with the use of a linchpin in the system to work on movements. It’s a type of device that detects someone’s physical activities controlled by the brain and responds accordingly. These devices are also termed as artificially intelligent devices as they are the simulations of human intelligence trying to move with the brain. Hover Board is the best example of a motion system with sensor acting on AI or we can rightly call it as “think to move” mobility device. Multiple technologies are used to detect movements in any motion-sensor system. Although artificial intelligence’s power of transformation is resonating across different industries, and in the healthcare industry, its footprint has created life-changing events. From clinical research to the curing process, from operation theater activities to ward’s projections, from drug development to insurance, AI applications are reorganizing the health sector, making it reach its new futuristic potential along with its work implementations, in-turn reducing the expenditure with better outcomes. Generally, robots are made with precision to surpass the human potential in scenarios tough to operate, along with to provide much better accuracy and quick results. They can carry weights that one human himself cannot. They can perform tasks at a speedy rate in comparison to humans. Being human our abilities are limited to M. Jindal (B) HoverRobotix, Ludhiana, India e-mail: [email protected] P. Garg Zayesha Creations, Faridabad, India e-mail: [email protected] K. Sood CT University, Ludhiana, India e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 M. Cardona et al., Exoskeleton Robots for Rehabilitation and Healthcare Devices, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-15-4732-4_2

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our physical strengths. But this is not the case with robotic devices as they are programmed to perform those specific tasks and work on provided information while using the calculative approach to respond in any situation. We have got this opportunity to extend our finite potential into infinite capabilities with robotic devices that are specially programmed. As the automated bots offer obvious activities, they can add more strength and they are designed to speed-up their processes easily. A device that uses AI and Robotics features can be counted as a logical future step in hospital routines. Those qualities are now needed specifically in a hospital environment that one might possess [6].

2.2 Sensors Are Becoming an Inherent Part of Our Routine Life In today’s lifestyle, we see intelligent sensor technologies are conquering everyday life and they are growing day by day. Opportunities that are named as innovative sensor technologies are up in each and every sphere of life whether it is a healthcare institute, a smart home, educational temple or the automobile industry. The vogue in our current life is driving innovation in health care with different technologies like noncontact radars, optoelectronic solutions lasers, surgical robots, AI-based analytical devices and much more.

2.2.1 Use of Sensors in Healthcare and Wellness Sector Sensor technology-based devices are getting inculcated in almost each and every area, combining new technologies and the social environment in order to improve quality living, health, fitness and much more with a wide range of applications. As an example, a tissue-integrated sensor enables us to monitor our body chemical changes in a radical way. These biosensor applications or devices are not isolated from our body but work with complete integration with our body tissue, and without any metallic body or electronics device used [3]. There are plenty of applications planned for health and wellness; specifically for chronic illness, these technologies offer easier and quicker solutions, thus offering comfort, safety and relief in our everyday life. Blood glucose measurement is an example that has the fastest increasing rate with high-tech sensor monitoring to generate accurate reports and help live significantly easier life for diabetic patients, especially those who are on insulin. These sensor fitted devices have their edge on the obsolete and older methods of observation, providing us with much more clear insights of glucose than ever before. There may be a case when a patient is motorically limited, maybe due to an accident or age factor; the KartBot (innovative AI-based form of a wheelchair) from HoverRobotix, is aspiring good times for such a patient. It is small, convenient,

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portable as well as light-weight making an immobile person, the man on wheels. All this has been made possible by consistent innovation, coupled with technology to provide quick, self-reliance solutions in health care as well as in the e-mobility sector. Similarly, the safety mats are an innovation to supplement conventional fall mats with the integration of sensors in then. These sensors register the load changes’ sequences, and on the basis of that data, they immediately respond to call for help [3]. There are woven fabrics with sensors technologies integrated to be used for risk patients, older people and for athletes too. Textiles with sensors can be used to monitor body movements; breathing for bedridden humans as well as for babies’ along with the help in regulating body temperature as well. Sensor devices are being used for many purposes: • • • • •

Fluid levels check in the body. Skin resistance check. Vital functions record like; heartbeat, breathing as well as temperature. Pressure Point detection and deformation [3]. Monitoring movement sequences [3].

2.2.2 Sensors Are Getting Deeply Embedded with Technology in Every Sphere of Life Making Life Much Easier and Better “Sensor technology will revolutionize the way we see the world around us”. India’s future belongs to the Artificially Intelligent systems as these devices are omnipresent in our everyday lives. More the areas of our lives are automated more the importance is increasing rapidly for the AI sensor technologies to build these futuristic devices. Our everyday life is transforming in every seven years and in times to come the process would be even quicker. These devices are being used to provide security, saving lives and improving quality living [3].

2.2.3 Sensor Technology and Motion Systems in Vehicles Currently used motion systems are based on sensors, robotic technologies and artificial intelligence that are becoming a driving force behind all the intelligent devices be it cameras, mobile phones, medical equipments, radar sensors, infrared or ultrasonic sensors, as well as mobility devices, making our everyday life easier plus increase the safety on the roads too. Automated parking assistants for vehicles with ultrasound sensors in the front/rearparts of vehicles to help handlebars in the parking. Systems

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are detecting the activities so well that if the car misses the parking space, the steering wheel locks in itself and the driver only has to operate the throttle and brake [3]. The upcoming models have the new innovative fully automatic feature to get parked by remote control and without the driver sitting in the car [3]. The cherry on the cake is big luxurious automakers are already offering a similar type of systems via smartphone applications. These innovative apps for parking sensors as well as for driving assistance are making everyday life much easier while finding a parking space in small towns and jam-packed cities. Luxury cars are one step ahead when it comes to autonomous driving. Some of those luxury sedans have a system to drive autonomously under special conditions in a short period. For example in the new models, engineers have the information combined [3] and gathered from the radar sensors resulting in an image of the environment in 360 degree mode around the vehicle [3]. It acts as an intelligent device to actively avoid collisions as well as it can alert to change the collision prone lanes, that act as an active side collision protection and prevents a collision.

2.2.4 Sensor Technology in Home and Garden There are wide ranges of applications available that are being used as sensor technologies in the smart homes, from fire protection through the control of heating, sensor-controlled burglar alarms and modern household lighting control systems, along with ambient climate-controlled houses. Also, there are a variety of robotic devices used for cleaning, that work independently and fantastically regardless of the floor type. Inbuilt camera sensors in these devices ensure cleaning at all the angles. Same way, these technologies are advancing at a much faster pace. The water damage prevention sensors are a good example of sensor technologies that are present in the dishwashers and help in saving the most valuable asset on earth. Its water sensor detects the flow of water coming in and going out and immediately beeps an alarm sound. Another application is sensor-controlled irrigation systems used in gardening. With these devices in place, watering the plants happens automatically that too only when the plants need water.

2.3 Sensors Are Changing the Vision of the Health Care Sector as Smart Sensors Are Initiating Will Appropriate Response, Accurate Analysis Along with Advising the Right Line of Treatment Smart sensors have transformed the healthcare industry to a great extent with each of their moves. You enter in any well-equipped hospital and you will be surrounded with sensors at every step and each state. From the entry gate to check-in your vehicle,

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from the ICU to laboratory and to the OPD you see and experience several varieties of devices that are working on AI technologies, programmed to perform and react on specific input, thus resulting in action that helps in handling the patients, the people and the work pressure of the staff with much ease and accuracy with the use of these much advanced sensors and futuristic technologies. The vent that is present on the ceiling having an air monitoring system that detects and reports visitor’s actions, while transmitting an airborne infection to a patient. There are many other sensors for the sinks and basins, required to wash hands before and after going into a patient’s room. Any identified carrier of pathogens that is swept with a handheld biosensor that identifies the specific organism and its pattern [5]. Tests once performed in a central laboratory are done by self-reporting chemical sensors implanted in ambulatory patients as well as those confined to bed. The bacteriology laboratory is being replaced by handheld biosensors; few of them are functioning as electronic noses and few used for detecting and characterizing organisms in sputum and other secretions [14]. The biochemical laboratories are getting replaced by chemical sensors implanted in patients. For those people who are acutely ill, the same chemical sensor array becoming integral component of infusion catheters. The values can be viewed by staff on the un-tethered devices from any location, and this data can also be gathered remotely and transmitted globally for better advice and opinion [5]. Patients do not need to tell their weight to staff. As, this information along with the vital signs, and blood chemistry values can be viewed on the staff’s handheld device too. The nurse informs a patient that this morning the toilet reported bacteria in urine and that the pharmacy will prescribe for the same accordingly [13, 5]. Central intensive care units have experienced a similar evolution, patients being transported on gurneys that serve as a vehicle, as well as an operating table, and additionally is a recovery bed too. Those multipurpose units equipped with sensors are monitoring vital signs of human body, blood and gas chemistries, and accordingly provide ventilation, suction and defibrillation. With the new technology, the central intensive care unit has been decentralized resulting in the disappearance of complications caused by cross-infection of other aggregated patients, which is an revolutionary step taking the healthcare sector to all the new levels of comfort [13, 8].

2.3.1 Motion-Sensor Technology Facilitating Elderly Patient Monitoring as Well as Specially Abled to Move Freely Motion sensors effectively detect early illness and lower the risks in seniors. According to a research in this regard, motion-sensor technology has emerged as a best tool that helps in reconstructing hospital processes and patient treatment therapy procedures. These systems allow the recognition and detection of movements with various health applications, including creating 3D maps rely on a depth-sensing camera that projects a grid of infrared beams and form a pattern to monitor behavioral studies and assess improvements or decline in health conditions of patients. Researchers

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created a fall detection system using Doppler radar. These systems identify changes in movements and signals increased fall risk, allowing the radar to recognize distinct fall signatures based on movement patterns. Computer networks used with wireless technology by these systems generating alerts according to movement patterns to suggest the patients for right medical interventions [4, 12].

2.3.2 Medical Delivery Robots in Hospitals Medical science is working with robots for women and children medical care as well. Those robots play an integral role in labor rooms while pregnant woman experiencing labor pains, in accordance with computer science and artificial intelligence laboratories. We have a new employee base in form of these intelligent machines called robots, working in hospitals 24 × 7 to carry out simple actions with the data analytics, like medication dispensing. It is still a question that if they can understand patient needs accurately and make scheduling decisions? As, every individual’s body acts differently with similar circumstances. The researchers have been working for the past 2 years to determine whether robots can be more than just helpful companions. It is going to be that one head nurse making these complex decisions [2]. It is a very hard job and a very high complex environment. The job requires responsibility for effective decision-making, from room assignment to patients along with right drugs and food distribution to all patients, nurses election to perform a C-section and much more. All should be done at a fast pace that too often in an unpredictable environment. Researchers trained a robot to learn from hospital staff like doctors and nurses for scheduling decisions. They learn why the doctors made that decision while opposing the other alternative solutions. The robot can observe and make an account on patterns with complexity of patients, to relate with the assigned staff and the break schedules or other information. It can check and arrange the staff with determining the availability of staff nurses on the floor. There is a study in which robot makes suggestions too for doctors and nurses on the basis of fed information and cases studies. The results of this study applied to a hospital as a decision-making system for assigning duties with the head nurse coordinating other nurses, attending multiple patients and in different rooms at the same time, which made the operations of the hospital even quicker, streamlined, effective and efficient. The researchers have many more use cases for this technology. It can be used as an effective training tool for novice nurses and can help make useful decisions in the labor ward at times [2]. As discussed earlier, this is a very big industry, and we have a vast range of intelligent sensor devices around us in hospitals. Some of them are known as helper robots, designed to do the specific tasks like fetching medicines, carrying documents and transporting things from place to place where they are needed, as these robots can carry a weight of approx. 330 kg in one building/floor. Earlier it was designed to carry only medicines, documents like stuff, but the staffs who used it at the hospital say, these are much more capable. As per the estimation at start, these robots can

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reduce around 1800–1900 km of footwork for hospital staff and help nursing to be done more efficiently without getting tired due to physical activities. Now the nurses can focus on tasks that are more important and take care of menial jobs. These robots have a tracking system that helps them to navigate the complex wings of the hospital while not bumping into anyone at all. A wide range of robots is being developed to serve in a variety of roles within the medical environment [6, 10].

2.3.3 Role of Motion Sensors or Mobility Aids in Medical Field Mobility aids are the devices designed to help more and more people having a problem in moving and enjoy freedom of independence. The people who have any kind of disabilities or the older people who are more on risk of falling while walking alone can use such mobility aids to make them boon to live with an ease and stability with a sense of independence [1]. Including the independence, mobility aid devices provide several more benefits to its users, in terms of reducing pain, and increasing self-confidence and self-esteem. A large range of e-mobility devices available in market as per the needs of people, starting from canes or crutches to wheelchairs and stair lifts, to hoverboards, mobility robots, Kartbots and much more [1].

2.4 Types of Mobility Aids Type of mobility aid which is required actually depends upon the issues of mobility with humans, or injuries or disabilities. There are a number of them available and some of the most common types include

2.4.1 Walkers Walker are made up of a framework build with metal having four legs to have stable base and to provide the strength to the chair and support to the user. These are counted as a very stable walking aids for almost every person who is in need of help while moving. The basic structure of a walker made with three-sided frame that surrounds and protects the user. These are so light weighted that users can easily lift and place the frame to move further and can step forward to meet it and repeat the process. There is an amendment in the device with wheels for those who cannot lift it up due to limited arm strength [1]. Types of walkers beyond the basic model include.

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Fig. 2.1 Rollator

2.4.2 Rollators This is also a common style of a walker with addition of seat and wheels with breaks to the walker frame, to provide the user more comfort, as while moving to long distances as compared to their regular ones they can rest as and when needed [1]. Knee walkers. It is similar to a rollator, but specifically designed with a padded cushion to allow the user to rest their weaker knee while propelling themselves forward with their stronger leg [1]. Walker–cane. This device is with a combination of a cane and a walker with a cross in-between, having two legs/canes unlike the walker having a full-frame. It is to be used for both with one or both hands working and it proves to be a greater support than any standard cane (Fig. 2.1) [1].

2.4.3 Wheelchairs We all are familiar with the device named wheelchair. A chair with wheels to help those people who are not able to walk on their own or not allowed to put weight on their lower limbs. They can be used to travel over greater distances or are more suitable for people with severe disabilities or temporarily when required or advised by physician in accidental cases. Also, the hospital staff uses these devices for moving the patients

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Fig. 2.2 Wheelchair

from one department to another for examinations. Wheelchairs can be manually propelled by the user himself, pushed by someone else or electrically powered also. An artificially intelligent wheelchair that can be propelled by the neural impulses of the patient sitting on it is an example of specialized types of wheelchairs. It includes standing wheelchairs and sports wheelchairs, along with customized wheelchairs as per the individual. This is the right example of a mobility aid (Fig. 2.2) [1].

2.4.4 Electric Wheelchairs Scooter Just like a wheelchair, electric wheelchairs scooters come with a seat set on top of either 3 or 4 or sometimes 5 wheels. It comes with a footplate to rest the user’s feet on, and trio handlebars or a steering wheel to direct and control the device movements. In these devices, a rechargeable battery is placed, which gives them power. This is more beneficial for those people who have the lesser upper body strength to use a wheelchair manually. It leaves a positive impact on their lives (Fig. 2.3) [1]. Safety Modifications Several modifications made in these devices to help navigate within a building or in areas with minimal changes in surface heights. These include Ramps for people who cannot manage stairs. Stair lifts for people to go up and down-stairs along the staircase channel. Hand-rails. used in restrooms or at entrances to provide support/stability.

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Fig. 2.3 Electric wheelchair scooter

2.4.5 KartBot KartBot is a device that combines the features of wheelchair with mobility scooters and turns the specially able people into a more confident, free and “Man on wheels” person. KartBot from HoverRobotix is converting the hoverboards into a seating arrangement with a chair and an added wheel to give complete support. It transforms hoverboards and mobility robots into a three-wheeled seated electric vehicle which is more stable and the control come into the hands with its two joystick arms (Fig. 2.4).

Fig. 2.4 KartBot (HoverRobotix)

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2.4.6 People Getting Benefitted from Mobility Aids People with mobility issues can benefit from any of these mobility aids shown above. Types of mobility aids depend on the needs of the person. These may benefit the person who is having problems like cerebral palsy, arthritis, diabetic ulcers, wounds, birth developmental disabilities, or the people having disabilities in maintaining correct balance, or sometimes with fractures or broken bones, heart or lung issues, or maybe any injury to the legs/feet/back, spinal injury, sprains/strains, visual impairment/blindness, etc. Older adults also take benefit from mobility aids [1]. Risks Although mobility aids provide many benefits to its users, still there is a risk of injury that can be associated with the use of the same without care. Like as, underarm crutches can cause crutch paralysis, due to excess pressure on the armpit nerves. Improper or excessive use of mobility aids may contribute to other injuries. Training is required for using the device correctly, which will include the risk knowledge as well as the steps to be taken care of. Assistance and training will surely reduce the risk and can lead to help more people with the new technology-based mobility aids or devices [1].

2.5 Hoverboards as an E-Mobility Device Used for Commuting, Maneuvering, Transportation, for Quick First Aid and Delivery of Medical Supplies, Carrying Patients and Most Importantly as Portable Wheelchairs The author of this chapter Munish Jindal, Founder & CEO of HoverRobotix is a social entrepreneur who is working day and night for humanity in every possible aspect of life. HoverRobotix is the first and only robotics organization of India into personal e-mobility robots, hoverboards, self-balancing electric scooters, Autobots and Kartbots. HoverRobotix brings the vision of the futuristic and personalized transportation in the realm of human life. HoverRobotix got established with the vision of bringing human race closer to the artificial intelligence of the robotics world by providing sustainable future to the upcoming generation through a green eco-friendly mode of transportation that is “hoverboards”. Munish Jindal [15] has introduced these devices that can bring a wonderful excitement in the life of every human being along with providing them a futuristic mode of transportation. The most incredible efforts done by him is the remarkable change he brought in the lives of specially abled by providing them portable emobility solutions along with it he has worked wonder for the hospital staff’s daily routine load reduction as well as bringing their footwork to minimum. We have seen that in multi-speciality hospitals or in large Govt hospitals the medical staffs have to move from one ward to another and one floor to another with speed carrying the

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medical trays and trolleys equipped with medical aids. With the use of HoverRobotix Products, they are able to move much faster, way more easily and can save many more lives with much active body as they will have less physical strain resulting in less tiredness. Also, as we have already briefed you about KartBot that is a combination of HoverBoarde-scooter and a wheelchair that works with the help of joystick arms. You can just push the handheld joystick arms forward and KartBot will start moving, the harder you push, the faster it goes. To stop the KartBot along with the hoverboard, just pull them back, and to reverse the KartBot and the hoverboard, just pull them back further. People are using them and calling it a life-changing product to transform their lives most wonderfully and positively. People love them as go-karts and the specially abled are using them as portable wheelchairs. There are all the safety features and warning systems inbuilt in every hoverboard/hoverkart to warn you, like for charging its battery to avoid any inconveniences. It has a battery indicator in the form of a LED light and a buzzer. It indicates you by emitting different colors to let you know about the current state of battery, like when the light is green it means the battery is full, when the hoverboard is on reserve power the battery light turns red and when the hoverboard is almost about to completely drain out of battery, the battery light starts flashing and the buzzer in hoverboard starts beeping. Hoverboards are portable electric devices that move on two wheels, powered by lithium-ion batteries that are rechargeable. The rider stands on a platform between the two wheels, which is large enough for the rider’s two feet and sturdy enough to carry his entire body weight. It works on artificially intelligent sensors and reflexes generated in the body by the human brain; hence, it is called “Think To Move Device”. When a rider thinks to move forward the brain automatically instructs the body and generates the reflex to leans forward, similarly backward, right or left, the wheels move in that direction. A series of sensors and highly sensitive pressure pads help catch the rider’s weight reflex and prevents them from falling forward with some balance involved. The sensors are so intelligent and pressure-sensitive that they can sense 200 movements per second.

2.5.1 Hoverboards Are the Futuristic E-Mobility Devices; Precisely We Can Say Future of Mobility, Decoding Future for Mankind Hoverboards use a variety of sensors and other components to move, balance, function and transport. • Gyroscope: This is the most important part of a hoverboard mobility scooters, this component adjusts the tilt of the platform in order to keep the rider balanced. • Micro Processors: These regulate the amount of power going to the wheels.

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• Battery: A lithium-ion battery made of numerous cells store the hoverboard’s electrical power and charge. 2 h usually gives a full charge and provide up to 2 h of continuous riding with a standby charge of lasting up to 1 month. • Hub Motors: Receives information from the logic board and powers the wheels according to the reflexes generated by the body of the rider. • Infrared sensors (IR sensors): Infrared rays measure an object’s presence and distance and are used for many functions. The infrared sensors in the hoverboard manage the stability and control of the rider as well as the device. • Tilt and speed sensors: Controls the speed of the hoverboard by accurately analyzing how quickly the wheels are turning and communicating with the gyroscope and the logic board. • Logic board: The main brain of the device. It processes the data from various sensors and sends the information to the motors. This provides the required movements and directions as well as a balanced device. • Pressure pads: This is where the rider’s feet are placed. They let the logic board know that someone is riding as well as what direction they want to go. • Two Wheels: These ranges in size according to the requirement and terrain. Different wheel sizes and shapes are available as per the rider requirement and the terrains to be operated upon. • Power switch: Turns the board on and off. • Charging port: To charge the hoverboard with the charger provided along with the device. In the case of KartBot in addition to the above components: • Chair: To sit comfortably on wheels. • Joy Sticks: Joysticks are used to control movements and allow a person with limited strength and ability to control it with their hands that too even better than a wheelchair. • Third Center Wheel: Just to provide more strength, safety, comfort and perfect balance to the person riding it.

2.6 Conclusions In health care, different types of sensing devices are used depending upon the characteristics, usability and efficiency. Nowadays in the medical field, patients take active participation in reviewing their reports. In this digitized world, various wireless communication standards have allowed the sensors to develop from traditional forms to help the patients in this regard. Number of passive sensors are used that constantly monitor individual patient’s vital signs and store that data. It can share that data wirelessly with healthcare professionals for opinion and observations as well as with the patient for his knowledge and review [9, 11].

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Sensors and motion devices are doing their part to make people healthier, smarter and more efficient medical solution and making health care more reliable and affordable. By giving patients more control over their health and extensive knowledge about their disease and symptoms, these devices are reducing the amount of time and money spent on expensive medical procedures and doctor’s visits. Plus, doctors and medical experts have access to real-time information about their patients, which helps them to provide them on-time treatment and that too more efficiently and in the coming era more of preventive health care measure would come in place due to much more advancement of technologies and lesser numbed of ailments for the mankind. This would also make the specially abled human beings commute much more effectively, to give them more freedom and self-reliance with an independent environment for living. Sensor technologies could very well prove to be the best medicine for making health care more affordable and accessible to everyone [7].

References 1. 2. 3. 4. 5. 6. 7. 8.

9. 10. 11.

12. 13. 14. 15.

J. Leonard, Mobility aids: Types, benefits, and use. www.medicalnewstoday.com S.A. O’Brien, MIT robot helps deliver babies. money.cnn.com A. Hengsberger, How sensors make our everyday life easier. www.lead-innovation.com R. Jimison. www.advisory.com C.B. Wilson, Sensors in medicine. www.ncbi.nlm.nih.gov Robots in hospitals are making deliveries and performing surgery. yellrobot.com R. James, Sensor technology is revolutionizing healthcare: comprehensive view. dzone.com C.M. Hu, C.H. Yeh, J.C.Y. Huang, Innovative design process for intelligent patient bed by using synergy of TRIZ and QFDs methods, in 2019 Fifth International Conference on Materials Research Advanced IoT based Smart Health Care System to Prevent Security Attacks in SDN (2012) D. Majumdar, P. Paramita Das, M. Nayak, Mobility-based real time communication in wireless sensor networks. www.ijcaonline.org T. Sprinkle, Sensors allow robots to feel sensation. www.asme.org A. Srilakshmi, P. Mohanapriya, D. Harini, K. Geetha, IoT based smart health care system to prevent security attacks in SDN, in 2019 Fifth International Conference on Electrical Energy Systems (ICEES) (2019) S. Kauffman, Miracles of life, marvels of technology. www.ihealthbeat.org www.medcertain.org C.B. Wilson, Sensors in medicine, BMJ M. Jindal. www.hoverrobotix.com

Chapter 3

Gait Capture Systems Manuel Cardona , José Yúdice, Francisco Huguet, Gabriel López, Cecilia E. García Cena, and Vijender K. Solanki

3.1 Introduction Angular kinematics consists in describing the temporal evolution of the different angles in 3D space. In gait analysis, hip, knee and ankle’s angle variation can put into evidence pathologies when these present relative variation in comparison to a normal gait. For capturing angular variations during gait, inertial measurement units(IMU) with wireless connection are presented as an appropriate alternative due to its portability, product of their small dimension and weight. We describe a new system for angular displacement data compilation and visualization for gait analysis. The system is composed of a set of 6 IMU with Bluetooth broadcast capabilities and software implemented with Max 7, an interactive platform. This program allows the visualization and recollection of the data broadcasted by the sensors. Figure 3.1 shows the schematics of the parts of the data capture system. The chapter is organized as follows: first a state of the art of the main motion capture system is presented and their features are analyzed. Then, the system proposed is described including the acquisition software. Finally, testing and results are discussed.

M. Cardona (B) · C. E. García Cena Centro de Automática y Robótica, Universidad Politécnica de Madrid, Madrid, Spain e-mail: [email protected] M. Cardona · J. Yúdice Universidad Don Bosco (UDB), Soyapango, El Salvador F. Huguet · G. López Universidad Centroamericana José Simeón Cañas, San Salvador, El Salvador V. K. Solanki Department of Computer Science, Engineering CMR Institute of Technology, Hyderabad, TS, India

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 M. Cardona et al., Exoskeleton Robots for Rehabilitation and Healthcare Devices, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-15-4732-4_3

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3.2 Motion Capturing Systems A motion capture system is a system used to record people’s motion in order to perform gait analysis [1], evaluate sport performance or even for visual effects studios. There are 4 types of motion capture systems according to the technology used: Optical-passive, Optical-active, Video/markerless and Inertial. Optical-passive systems use retro-reflective markers that can be tracked by infrared cameras, this is the most common method used for motion tracking. Optical-active systems utilize LED markers that are tracked by special cameras; they require a battery or charge of some kind. Video/markerless systems rely on software power to track movement, even though there are different methods of doing this method incurs in more errors that marker based systems. Finally, inertial motion capture system rely the usage of inertial mass units, usually called IMUs, rendering unnecessary to use cameras, the IMUs are attached to the subjects body and they send data to a computer wirelessly. There are many companies that provide motion capture systems, Table 3.1 shows a small comparison between the systems, to a different array of institutions and universities, not only for research purposes in biomechanics laboratories but for other type of uses like, gaming, animation, virtual reality, clinical diagnosis and others, motion capture systems have come a long way and it’s technologies vary in different aspects, from software to hardware, advancements in technologies like

Fig. 3.1 Motion capture system diagram

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video cameras and wireless wearable devices have made it possible for these systems to have high accuracy and information processing, in this section we’ll review motion capture systems from Vicon, MotionAnalysis, Optitrack, Qualisys and Codamotion.

3.2.1 Vicon Vicon Motion Systems is a company that develops mainly optical-passive motion capture systems that have already been used in a variety of studies for people with strokes and to validate other motion capture systems [2–4], it offers a variety of technologies, including software and hardware for virtual reality, however for biomechanics and gait analysis their main software are called, Nexus, Tracker and Shogun although the last one is mostly used for VFX like the ones you’d use in gaming. Nexus is their front liner software for Biomechanics, sport science and clinical motion capture. Vicon Motion systems also offers a variety of 6 types of cameras, as shown in Fig. 3.2, useful for motion tracking, however the Viper and ViperX models are advertised for virtual reality applications. In addition to the optical-passive system, Vicon Motion Systems also offer an IMU system called Blue Trident, a picture of the IMU is depicted in Fig. 3.3, this lightweight IMU (12 g) can be used in conjunction with the nexus software for more accurate data capturing applications. Another advantage of this IMU is that it can be used underwater to a maximum depth of 1.5 m for up to 30 min though it has been tested at depths of 3 m for a time period of an hour according to the manufacturer.

Table 3.1 Motion capture systems features Company Software Type Vicon

Markers

Capture system

IMUs

Opticalpassive Optical passive/active

Reflective

IR camera

Blue tradent

Motion analysis

Nexus Tracker Shogun Cortex 8 BaSix

Reflective LED

–

Optitrack

Motive

Optical passive

Reflective

Qualisys

Qualysis tracker manager ODIN/RTNet SDK

Optical passive

Reflective

IR camera/sensor camera IR camera/color camera IR camera/MRI

Codamotion

Optical active Reflective

–

Delsys Cometa Noraxon Sensor camera – indoor/outdoor

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3.2.2 Motion Analysis Motion analysis is another company that creates motion capture systems which have been used in gait analysis [5–7]. The software promoted by this company is called CORTEX 8 which is an optical-passive system that works with markers over the body, but also, they have a version called BaSix which is an optical-active motion capture system that utilizes LED arrays that don’t require a suit and are easy to attach. As for cameras, they offer 5 different types of cameras as shown in Fig. 3.4, which don’t vary much amongst them, they only differ in resolution, frame rate, number of pixels, FOV and Lens, the most powerful camera being the Raptor 12HS with a 4096 × 3072 resolution and 12.5 MP.

3.2.3 Optitrack Optitrack is another system capable of motion capturing and it has been applied in studies of human movements [8], the software offered by the company is called “motive” and it is primarily an optical-passive type of system, it also offers connection capabilities with gaming engines and modeling programs like MotionBuilder and Maya, when it comes to cameras, Optitrack has a varied arrange, In Fig. 3.5, we observe their main power cameras raging from a resolution capacity of 1.3 MP up to 4.1 MP, however they also possess a more comfortable line of cameras, price wise, that can be obtained for motion capture as well, we see these cameras in Fig. 3.6, with a range of resolution from 0.3 MP up to 1.3 MP, which obviously makes them less powerful that their previous siblings but for a more reasonable price.

Fig. 3.2 Vicon Motion Systems camera types for motion tracking. a Vantage, b Vero, c Vue, d Vertex, e Viper, f ViperX

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Fig. 3.3 Vicon Motion Systems Blue Tradent IMU system, single sensor with attachable wristband

Fig. 3.4 Motion analyisis camera models. a Kestrel 4200, b Kestrel 2200, c Kestrel 1300, d Kestrel 300, e Raptor 12HSd

3.2.4 Qualisys Another company that provides motion capturing system is Qualisys and just like other systems, it can be used for sports, human biomechanics, engineering, entertainment and animal biomechanics, the software that they provided is called “Qualisys Track Manager” and it’s primarily an optical-passive system, it can also be connected to game engines like Unreal and Unity as well as software like MotionBuilder, Maya, MATLAB and others. Another advantage of the Qualisys system is that it can be integrated with IMUs from different manufacturers like Delsys, Cometa/Myon

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and Noraxon and it also provides simultaneous motion tracking along with EMG recording devices. Qualisys also possesses a wide variety of cameras even going as far as offering underwater cameras and MRI compatible cameras to record patients at the same time they are in the MRI machine (Fig. 3.7).

3.2.5 Codamotion Codamotion is the last system we will be reviewing, it is an optical-active system that’s been used in gait analysis studies, [1, 9, 10], with very peculiar type of markers, and that’s what differentiates this system with the others, also it is very portable and it only needs 2 sensors (cameras) to work properly. ODIN is their front liner software for motion capture with a friendly and easy to use graphical interface as shown in [11]; however they also offer software called Codamotion RTNet SDK for programmers,

Fig. 3.5 Optitrack camera models. a Prime 41, b Prime 17W, c Prime 13, d Prime 13W, e Prime Color

Fig. 3.6 Optitrack low cost camera models. a Flex 13, b Flex 3, c Slim 13E, d Slim 3U

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engineers, and system integrators based in C++ and Java which will allow them to make real-time 3D measurements. Another difference with the Codamotion system is that it uses sensors in a different configuration than your traditional motion capture camera, Fig. 3.8, show both models that they currently offer, the CXS model is thought to be an outdoor sensor unit that can be used for sports analysis and other experiments to be performed outside. Markers are another very characteristic feature of the Codamotion system, where the others systems usually use reflective markers and IF cameras, Coda uses an array of LEDs that can be configured using clusters. Figure 3.8, shows a different array of images that depict markers and clusters used with the Coda system, each marker

Fig. 3.7 Qualisys camera models. a Miqus Hybrid, b 5+, 6+ and 7+ series 3, c Miqus, d Miqus video, e Underwater, f MRI

Fig. 3.8 Codamotion sensor units. a CX1, b CXS

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emits IF light, the standard and high powered markers work just like the reflective markers of the previous systems in which they are placed at specific points that the sensors can track, however the clusters are solid structures that are attached to a part of the body, each cluster consist of 4 light emitting markers. The system also make use of frames and gait wands, these are structures that protrude from the desire part of the body to be studied, for example the legs, and they are tracked by the sensors, these were specifically design for the CODA system. An example of a frame can be seen in Fig. 3.9e.

3.3 The Proposed System The proposed system consists of 3 wireless Inertial Measurements Units (IMU) developed by Witmotion company (model BWT901CL as shown in Fig. 3.10). Each wireless IMU includes a high precision three axis gyroscope, three axis accelerometer, three axis geomagnetic sensor, and a 32 bit high performance MCU. Moreover, the units feature a rechargeable lithium battery of 150 mAh deeming it unnecessary to have an external power source connected to it during data collection. The data provided by the sensor are 3D space angles, angular velocity, angular acceleration, magnetic field and time. The broadcast is emitted at a rate of 115,200 bauds by default. The maximum reach to guarantee a good broadcast is of 10 m and the angular resolution of the sensor is 0.05 ◦ . The variables recorded for each leg are, hip flexion and extension, knee flexion and extension and ankle’s angular position. Ever variable is measured in degrees and 6 sensors are required, 3 per leg at hip, knee and ankle level. The sensors are attached to the legs using a system of Velcro belts for easy removing and attachment as seen in Fig. 3.11.

Fig. 3.9 Codamotion markers. a Standard Marker, b High-powered Marker, c Mini cluster, d Cluster, e Pelvic frame, f Acromion cluster

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For hip flexion/extension a sensor is attached on the thigh (cuadriceps femoralis), for knee flexion/extension the sensor is attached over the tibia and finally for ankle angle position the sensor is attached over the dorsal part of the foot. Seeing how the sensors can measure angle in 3D space, it is necessary to define a reference plane so that we don’t have to deal with so many variables per sensor, for this reason they will be attached in a parallel orientation to the coronal plane this allows to record angle changes prominently in the transversal plane or x axis. Therefore the data collected and used for analysis will only be the one obtained from the x reference axis, Fig. 3.12. Details the position of the sensors.

Fig. 3.10 Sensor Gyro 2.0 BWT901L by Witmotion company Fig. 3.11 View of sensors used in the experiment

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Fig. 3.12 Sensor position over the leg of the subject

3.4 Data Acquisition Software 3.4.1 Development Platform The data collection software was implemented using the visual programming platform for multimedia interactive applications Max 7. The platform allows information treatment obtained from various types of sensors and the design of machine-man interfaces with great flexibility. Max 7 also facilitates the creation of standalone programs for Windows and Mac. Figure 3.13. Shows the interface of the program and some of the visual programming code is shown in Fig. 3.14.

3.4.2 Software Features The motion capture system possesses the following features:

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• Real time reception and deployment of time information up to 6 BWT901L sensors connected wirelessly simultaneously to the computer. • Variable tracking: angular acceleration, angular velocity, angle in all 3 axis (x, y, z). • Relative angle calculation between sensors. • Real time graphic of the variable behavior. • Reference position setting for the studied variables. • Recording of the data acquired in a .txt file. • Reproduction and animation of a sequence of data saved in a .txt file.

Fig. 3.13 Motion capture software interface

Fig. 3.14 Example of visual programming in Max 7, Small part of the motion capture program

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3.4.3 Sensor Data Reception and Processing The information sent consist of hexadecimal data chains, each linked to a variable. The data are recovered by a serial port and read at a rate of 100ms by the motion capture program. Every chain recovered consists of 11 bytes with a format shown in Table 3.2. The first byte indicates the chain’s beginning, the second byte indicates the associated variable, the third and fourth byte indicate the value of the variable in the x axis, the fifth and sixth byte indicates the value of the variable in the y axis, the seventh and eight byte indicates the value of the variable in the z axis, the ninth and tenth byte indicate the value of the sensor’s temperature and the last byte is a checksum to guarantee the integrity of the data chain. As shown in Table 3.2, the value of the variables in the x, y and z axis are sent in two bytes (lesser and greater significance), to recuperate the decimal value of the variable it is necessary to: • The composition on the 2 byte word through a displacement operation and a bit to bit Or operation between the less and more significant variable bytes. The result of this operation in a 4 digit hexadecimal number with a sign bit. • Conversion of the 4 hexadecimal digits into decimal digits. • Multiplication by a factor of resolution and scaling proper to the variable analyzed. Table 3.3, expresses in detail the treatment of information sent from the sensors for the calculation of the variable values and Fig. 3.15 shows a portion of the code used for these calculations.

Table 3.2 Data chain format broadcasted by the BWT901L via Bluetooth and received by the motion capture software Accelaration

0 × 55

0 × 51

AxL

AxH

AyL

AyH

AzL

AzH

TL

TH

Checksum

Velocity

0 × 55

0 × 52

wxL

wxH

wyL

wyH

wzL

wzH

TL

TH

Checksum

Angle

0 × 55

0 × 53

RollL RollH PitchL

YawH

TL

TH

Checksum

PitchH YawL

Table 3.3 Acceleration, velocity and angle computations Angular acceleration ax = ((AxH8)| AxL)/32768*16 g ay = ((AyH8)|t AyL)/32768*16 g az = ((AzH8)| AzL)/32768*16 g Angular velocities wx = ((wxH8)|wxL)/32768*2000 (◦ /s) wy = ((wyH8)|wyL)/ 32768*2000 (◦ /s) wz = ((wzH8)|wzL)/ 32768*2000 (◦ /s) Angle Rollx (x axis) = ((RollxH8)| RollxL)/32768*180◦ Pitchy (y axis)= ((PitchyH8)| PitchyL)/32768*180◦ Yawz (z axis) = ((YawzH8)| YawzL)/32768*180◦

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Fig. 3.15 Portion of the code used for acceleration calculation

3.4.4 Data Deployment and .txt File Generation Through a series of menus in the program, as seen in Fig. 3.16, the user can select for each sensor a variable and an axis to be monitored. The user’s selection filtrate the data chain sent by the sensors selecting only the bytes of interest for their treatment and deployment. For each connected sensor the instant value of the variables and its evolution are deployed by the program in a graphical and numerical way as shown in Fig. 3.17. The program allows for the generation of a .txt file delimited by tabulation from a sequence of recorded data. For each recorded variable it is possible to edit within the program a label for the identification of the position of the sensor y the subject’s body. Within the .txt file the following data can be found for teach of the 6 sensors: • Saved data series index. • Time stamps for the recorded data.

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Fig. 3.16 Variable and axis selection screenshot. a Variable selection, b axis selection

Fig. 3.17 Instant value and behavior graphic of the selected variable

• Studied variable and axis. • Sensor’s position identification label. Thanks to the fact that the text file is delimited by tabulation, it is possible to transfer its content into a excel sheet by just copying and pasting the data.

3.5 Testing and Results The system was tested from the analysis from the data collected from subjects with normal gait and its posterior comparison to profiles of normal gait present in the literature [1, 12–14]. For each data recording the following steps were followed: • Bluetooth connection between the sensors and the motion capture program and selection of angle variable. • Sensors positioning over the subjects legs, for each leg a sensor at thigh, tibia and foot level. • Establishment of reference angular position for all sensors with the subject standing. • Walking of the subject in a straight line (5 meter) and data capture. • Text files generation with the captured data.

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Fig. 3.18 Flexion and extension of hip, knee and ankle (left leg)

Fig. 3.19 Flexion and extension of hip, knee and ankle (right leg)

• Curve comparison between the obtained curves and the normal gait profile from literature. Graphic of the angular variation in normal gait data captured in a study session can be seen in Figs. 3.18 and 3.19. It can be observed that maximum hip flexion and extension is between 30 and −15◦ respectively, meanwhile maximum knee flexion and extension is between 55 and −10◦ respectively, finally the maximum ankle flexion and extension is between 60 and −26◦ respectively. The previous values correspond to the values reported in the literature. From these results and analysis of other data recording sessions we can conclude that our wireless system for kinematic gait motion capture provides adequate values.

3.6 Conclusions In this work the state of the art of motion capture systems used for gait analysis has been presented showing the main characteristics for each one of them. In addition, a new wireless motion capture system was presented which is composed of wireless inertial sensors of nine axes: each module consisted of a high precision three axis gyroscope, three axis accelerometer, three axis geomagnetic sensor, and a 32 bit high performance MCU. According to the manufacturer, the IMU allow us to have an output rate up to 200 Hz and an accuracy of 0.05 ◦ .

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In order to validate the system, several tests were carried out with volunteers, the results obtained by our motion system agree with previously published works. Finally, the proposed system is an important tool for kinematics gait analysis of the human movement both for healthy people and pathological patients.

Acknowledgements The authors would like to thanks to the Government of El Salvador for the support through the “Fondo de Investigación de Educación Superior (FIES)". Manuel Cardona would like to thank to Fundación Carolina and Universidad Don Bosco for their support during this research.

References 1. M. Cardona, C.E. García Cena, Biomechanical analysis of the lower limb: a full-body musculoskeletal model for muscle-driven simulation. IEEE Access 7, 92709–92723 (2019). https:// doi.org/10.1109/ACCESS.2019.2927515. 2. F. Wang, E. Stone, M. Skubic, J.M. Keller, C. Abbott, M. Rantz, Toward a passive low-cost in-home gait assessment system for older adults. IEEE J. Biomed. Health Inf. 17(2), 346–355 (2013). https://doi.org/10.1109/JBHI.2012.2233745. March 3. N. Anang, R. Jailani, N.M. Tahir, H. Manaf, N. Mustafah, Analysis of kinematic gait parameters in chronic stroke survivors, in IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), Batu Feringghi, vol. 2016 (2016), pp. 57–62. https://doi.org/10.1109/ ISCAIE.2016.7575037 4. M.R. Kharazi et al., Validity of microsoft kinectTM for measuring gait parameters, in 2015 22nd Iranian Conference on Biomedical Engineering (ICBME), Tehran (2015), pp. 375–379 5. M.P. Kadaba, H.K. Ramakrishnan, M.E. Wootten, Measurement of lower extremity kinematics during level walking. J. Orthop. Res. 8(3), 383–392 (1990) 6. J. Perry, J.R. Davids, Gait analysis: normal and pathological function. J. Pediatr. Orthop. 12(6), 815 (1992) 7. D. Sutherland, The evolution of clinical gait analysis: Part II Kinematics. Gait & Posture 16(2), 159–179 (2002) 8. G. Nagymate, R. Kiss, Application of OptiTrack motion capture systems in human movement analysis A systematic literature review. Recent Innov. Mech. (2018) 9. K.J. O’Donovan, B.R. Greene, D. McGrath, R. O’Neill, A. Burns, B. Caulfield, SHIMMER: a new tool for temporal gait analysis, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN, vol. 2009 (2009), pp. 3826–3829 10. D. Jarchi et al., Assessment of the e-AR sensor for gait analysis of Parkinson’s Disease patients, in 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA (2015), pp. 1–6 11. M. Cardona, C.E. García Cena, F. Serrano, R. Saltaren, ALICE: conceptual development of a lower limb exoskeleton robot driven by an on-board musculoskeletal simulator. Sensors 20, 789 (2020) 12. M. Cardona, C.E. García Cena, Musculoskeletal modeling as a tool for biomechanical analysis of normal and pathological gait. in VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering. CLAIB 2019. IFMBE Proceedings, vol. 75 (Springer, 2019), pp. 955–963 13. A. Kapandji, Fisiologia Articular, vol. 2, 6th edn. (France, Editorial Panamericana, 2010) 14. S.R. Ward, R.L. Lieber, S.L. Delp, A model of the lower limb for analysis of human movement (2010)

Chapter 4

Technologies for Therapy and Assistance of Lower Limb Disabilities: Sit to Stand and Walking Isela Carrera, Hector A. Moreno, Sergio Sierra, Alexandre Campos, Marcela Munera, and Carlos A. Cifuentes

4.1 Introduction According to the World Health Organization over a billion people live with some form of disability [1]. The quality of life and autonomy of people with disabilities, particularly those related with aging, is one of the major challenges that society will face in the next decades. Low birth rate and the increase of life expectancy are key factors that have contributed to the growth of the aging population. The number of

The original version of this chapter was revised: Author list has been updated. The correction to this chapter is available at https://doi.org/10.1007/978-981-15-4732-4_6. I. Carrera (B) · H. A. Moreno Universidad Autónoma de Coahuila, Barranquilla S/N, Col. Guadalupe, 25720 Monclova, Coahuila, Mexico e-mail: [email protected] H. A. Moreno e-mail: [email protected] S. Sierra · M. Munera · C. A. Cifuentes Colombian School of Engineering Julio Garavito, Autopista Norte AK 45 No. 205-59, 111166 Bogota, Colombia e-mail: [email protected] M. Munera e-mail: [email protected] C. A. Cifuentes e-mail: [email protected] A. Campos Universidade do Estado de Santa Catarina, Avenida Lourival Cesário, s/n, Edifício Alcides Abreu, Nova Esperanza 88336-275, Brazil e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020, corrected publication 2020 M. Cardona et al., Exoskeleton Robots for Rehabilitation and Healthcare Devices, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-15-4732-4_4

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persons aged 65 years or over is projected to be 1.5 billion in 2050 [2]. This group of population will increase the demand in health care, caregiving, and medical expenses. The development of robotic rehabilitation systems has been considered a solution to this problem. Rehabilitation robots are generally classified into therapy robots and assistance robots. Therapy robots can apply a consistent treatments for long periods of time helping patients to learn, or relearn how to move since human neuromuscular system exhibits use-dependent plasticity [3]. On the other hand, assistance robots help people with mobility impairments to complete activities of daily living. Those activities include holding or moving objects, interaction with other devices, and transportation. Assistance robots are employed to facilitate the development of daily life activities, those devices represent augmentative tools for users with residual mobility capacities. On the one hand, this chapter presents an overview of the robotic devices for therapy and assistance of lower limb disabilities that have been reported in the literature, particularly those related to the sit to stand (STS) movement and walking. On the other hand, causes of motor disabilities and typical rehabilitation therapies are reviewed. The purpose of this document is to provide relevant information that should be considered for designing and evaluating rehabilitation robots for lowerlimb disabilities. The following section describes those diseases related to motor disabilities. A description of biomechanics of the sit to stand task and walking is made later. Then traditional rehabilitation therapy techniques are briefly discussed. Finally, a survey of assistance and rehabilitation robots for sit to stand task and walking is presented. This document presents different robotic devices for these purposes, and it is regarded as a chapter that complements the subject of this book.

4.2 Conditions Affecting Mobility Human mobility is often considered as a key capacity for individuals’ development and well-being, as it provides autonomy and independence during everyday activities [4]. It involves not only the musculoskeletal system but also several dissociable neurological systems [5]. For instance, human mobility involves the activation of the central nervous system (CNS), muscular activation, and the integration of sensory information [6]. Affectations to the proper functioning of these systems, could result in disorders or limitations of individuals’ mobility [7]. In general terms, mobility impairments are usually defined as a category of disability, including several types of physical and sensory impairments [8]. Similarly, several authors define mobility impairments as the difficulty for walking or inability to ambulate without a significant complication or assistance [9–11]. Different health conditions and pathologies affect the key components of mobility [12], such as balance, control, and stability [13]. These alterations can be broadly classified into neurological or musculoskeletal causes. Among the neurological pathologies, Stroke, Spinal Cord Injury (SCI) and Cerebral Palsy (CP) are com-

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monly found to be strongly related to locomotion impairments in adultness and childhood [4]. Likewise, the progressive deterioration of the cognitive functions [14] (i.e., sensory deficits and coordination difficulties [15]) and the neuromuscular system in older adults [16] (i.e., loss of muscle strength and reduced effort capacity [15]) are commonly related to the partial or total loss of locomotion capacities. As a general overview, the World Health Organization (WHO) identified that the proportion of the mobility impaired population has experienced constant and major growth in the last years [17]. Specifically, it has been reported that nearly 15% of the world’s population experiences some form of disability [18]. Moreover, demographic predictions state that by 2050 the proportion of the world’s population over 60 years will nearly double from 12 to 22% [19, 20]. These studies also report that a larger percentage of this growth will take place in developing countries [19]. The prospects for developing countries, such as Latin American countries, are often accompanied by social, economic and public health factors that generally hinder the access to assistive technologies or rehabilitation solutions [21, 22]. For instance, the WHO reported that the 50% of people are not able to afford health care nor access to rehabilitation services [18]. United Nations (UN) estimate that nearly 386 million of the working-age people suffer from some kind of disability and the unemployment rates among these population is as high as 80% in some countries [23]. Moreover, in low- and middle-income (LMI) countries there is a large unmet need to be addressed, regarding the workforce of physicians and rehabilitation professionals [24]. Against this backdrop, the identification of the causes and conditions that lead to mobility limitations is a public health issue and should be thoroughly analyzed. This analysis should be aimed at identifying new solutions and strategies for the rehabilitation and assistance of people with mobility impairments. In this regard, the main conditions and pathologies that lead to mobility limitations are further described and analyzed.

4.2.1 Cerebrovascular Accident or Stroke Normal functioning of brain relies on the adequate perfusion of oxygen and nutrients to its different tissues. Alterations to proper behaviour of cerebral vessels (e.g., the blockage of blood flow) can result in a cerebrovascular accident or Stroke [25]. In general terms, Stroke can be divided into two categories: (1) ischemic stroke, caused by a blood clot that blocks the bloodstream in the brain, and (2) hemorrhagic stroke, caused by a ruptured blood vessel that bleeds into the brain [26]. Specifically, ischemic strokes account for the 85% of cases, whereas 15% are due to hemorrhages [27]. Depending on the affected region after the occurrence of a stroke, different groups of neurons die as a result of the blockage of oxygen and nutrients [25]. Accordingly, the functions provided by those brain cells, could be permanently affected or lost [27]. After a stroke, the following consequences are commonly found: (1) movement and motor control disorders, (2) sensory and perception impairments, (4) chronic

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pain, (3) language problems, and (4) cognitive disturbances [28, 29]. Regarding motor control, paralysis is often presented at the opposite side of the body of the affected side of the brain. One-sided paralysis is referred as hemiplegia and onesided weakness is called hemiparesis [29]. The effects of body paralysis usually result in difficulties in everyday tasks, such as walking and grasping [30]. Specifically, the occurrence of a stroke might result in unnatural gait patterns, characterized by decreased walking speeds, asymmetrical gait cycles, poor motor unit activation, and reduced muscle capacity [31]. Moreover, sensory deficits and cognitive impairments in stroke survivors also affect the key characteristics of healthy gait [32]. Stroke survivors usually exhibit serious problems regarding balance, coordination, muscular spasticity and propioception [32]. Among the different risk factors of stroke, several birth conditions, diseases, and lifestyle factors are found [31]. • Non-modifiable factors or birth conditions: – Birth weight: Different studies have reported that low birth weight is associated with increased risk of stroke in adultness [33]. – Gender: Although each individual risk factors are dominant and more evident, stroke risk is often 1.3 times higher for men than for women [34]. – Age: Due to the cardiovascular condition in elderly population and co-morbidity of several health conditions, the incidence of stroke considerably increases with age [35]. – Ethnicity: The incidence of stroke has been proven to vary between different ethnic groups. Specifically, Caucasians exhibit lower risk of stroke compared with people from African origin [31, 36]. – Genetic causes: Clinical observations have concluded that different inherited characteristics might predispose the occurrence of a stroke [37]. • Diseases and measurable factors – Cardiovascular conditions: Several studies have shown that some cardiovascular diseases increase the risk factor for stroke [38]. For instance, hypertension is the most important treatable risk factor and it is commonly detected among stroke patients under 55 years of age [39]. Similarly, the atypical contractions of the atrium (i.e., atrial fibrillation) usually lead to non-laminar blood flow, developing blood clots that could embolize to cerebral arteries [31]. Moreover, the presence of silent cerebral infarcts also increases the stroke risk by at least two times from other vascular risk factors [40]. – Recurring cerebral attacks: Previous strokes or transient ischemic attacks (i.e., an ischemic deficit that resolves rapidly) constitute powerful risk factors for stroke [31]. – Various medical conditions: There are many conditions that have been studied and suggested to be related with increased risk for stroke. Among these conditions, coagulation disorders, diabetes mellitus, migraine, renal disease, inflammatory diseases and different surgical procedures are commonly found [31].

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• Lifestyle factors: Several everyday aspects have been reported whether as stroke risk factors or acute triggers of stroke onset. Among these, the following are usually found: (1) smoking [41], (2) excessive alcohol consumption [42], (3) drugs abuse (i.e., due to pathogenic mechanisms) [43], (4) obesity [44], (5) stress [45], and (6) socioeconomic factors [46]. In contrast, physical activity and balanced diet are often related to a lower stroke risk [31, 47]. According to worldwide statistics, the cerebrovascular accidents constitute the second common cause of death and the third leading cause of long-term disability in adultness [48, 49]. Several global reports state that nearly 15 million people suffer stroke every year, of which 5 million result in death and another 5 million are permanently disabled [50–52]. As found by a recent systematic review of literature, the worldwide stroke incidence rates exhibited a significant 42% decrease in highincome countries. However, during the same period, the incidence rates in LMI countries increased by over 100% [48, 49]. Moreover, the same study reported that the number of disability-adjusted years (DALYs)1 was nearly seven times higher that in high-income countries [48, 53, 54].

4.2.2 Spinal Cord Injury (SCI) The CNS is mainly constituted by the spinal cord and the brain. Specifically, the spinal cord is made of nerve fibers that transmit impulses back and forth between the body and the brain [55]. Thus, it serves as a center for initiating and coordinating many reflex acts, as well as motor and sensory functions (i.e., working along with the peripheral nervous system) [56]. In this sense, damages or traumas to the spinal cord or nerve roots, including the cauda equina (i.e., the end of the spinal canal), often results in permanent loss of movement, sensation and other body functions below the site of the injury [55–57]. In general terms, a SCI begins with a sudden, traumatic blow to the spine that fractures the vertebrae. Regarding the causes of the SCI, the National Spinal Cord Injury Association (NSCIA) reports vehicle accidents as the leading cause of injury in young people, followed by falls (i.e., the main cause after the age of 65), violence acts, and sports [57]. Moreover, the causes of SCI also include diseases (e.g. cancer, arthritis or osteoporosis), infections, blockage of blood supply, and compression by fractured bones or the presence of tumors [58]. The severity or completeness of the SCI defines the loss of functions in the individual, and is usually classified as either complete or incomplete [55]. A complete injury refers to the total loss of motor and sensory function below the level of injury [59]. An incomplete injury occurs when a certain degree of functioning remains below the injury [59, 60]. Similarly, considering the letter-and-number name of the vertebra at the injury site, the level and effects of the SCI are defined [58, 60]. 1 Years

of life lost due to premature death and years lived with a disability of a specified severity and duration. Each DALY can be understood as 1 lost year of healthy life [48].

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According to the WHO, between 250.000 and 500.000 cases of SCI are reported around the world [61] and nearly 18.000 new SCI cases occur each year [62]. An epidemiological study found that the proportion of SCI patients doubled between 2003 and 2011 [63]. Moreover, worldwide statistics report an annual global incidence of 40 to 80 cases per million, where the 90% of these cases are due to traumatic causes (e.g. car accidents and falls) [61]. Similarly, it has been stated that 80% of new cases are male, and individuals between the ages of 16 and 30 are more likely to suffer a traumatic SCI [61, 62].

4.2.3 Cerebral Palsy (CP) The most common cause of disability in childhood is associated with the Cerebral Palsy. This condition is often characterized by a group of disorders affecting the movement, the muscles tone an the posture of the individual [64]. This affectations are also accompanied by deficits in sensation, perception, cognition, communication, and behavior [64]. Frequently, CP is the result of abnormal brain development or damage to the developing brain [65]. Although brain damage usually occurs before birth, it can also take place at birth or during the first years of life [66]. The symptoms of CP are not unique, as they affect each person differently [65]. Thus, CP can be classified according to the main type of movement disorders involved. In general terms, there are four types of CP: spastic, dyskinetic, ataxic and mixed [65]. For instance, spastic CP is the most common type and represents the 80% of cases. This type of CP is characterized by increased muscle tone and can be presented on each side of the body both independently or jointly [65, 67]. People with spastic CP often exhibit problems during standing and walking [68]. Similarly, dyskinetic CP causes problems in the movement and control of hands, arms, feet, and legs, generating difficulties to sit and walk [69]. People with ataxic CP exhibit balance and coordination disturbances, and thus, they usually present unsteady walking [70]. Finally, people with mixed CP present symptoms from more than one type of CP [65]. According to worldwide reports, the prevalence of CP ranges from 1.5 to 4 cases per 1,000 live births or children of a defined age range [71]. Moreover, around 764.000 people currently live with CP, from which 500.000 are children and teens [72].

4.2.4 Elderly With aging, different health conditions affect people’s well-being and overall autonomy. In general, more than half of adults over the age of 65 have three or more medical problems [73]. For instance, older adults often exhibit cardiovascular complications, hormonal disturbances, cancer or musculoskeletal diseases [16, 74]. Similarly, it has been identified that advanced age is a common risk factor for several cardiovascular

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and neurological diseases [75]. Therefore, the assessment and treatment of the health status of older adults is sometimes challenging [76]. Among the different disorders that occur as people age, mobility and balance are commonly compromised. Specifically, the natural gait pattern and quality are affected by problems in the nervous system, the musculoskeletal apparatus, and the cardio-respiratory system [12]. Additionally, further injuries and severe damages may occur, as balance and gait disorders significantly increase the risk for falls [12, 77]. There are different and important indicators that determine the quality of gait, such as, walking speed, cadence (i.e., number of steps or gait cycles per unit of time), step length, stride length (i.e. linear distance covered by one gait cycle), among others [12]. In this sense, older adults’ gait is usually characterized by decreased speed and step length, while cadence remains stable compared to younger people [12, 78]. Moreover, in order to ensure stability, elderly subjects with gait problems prefer shorter step lengths, as well as, wider stance phases [12]. Similarly, several studies have demonstrated that vision, memory, and cognition, have strong effects on individuals’ gait and overall independence [75, 79]. Specifically, it has been reported that cognitive control is relevant for obstacles overcoming, safe navigation in the environment, as well as, for choosing optimal routes while walking [10, 12, 75]. Likewise, individual’s proprioception, visual-spatial perception and attention have been also identified as key factors for safe gait and fall prevention [12, 80]. Several psychological factors also influence gait, such as depression and anxiety, which lead to an overly cautious and slower gait [12]. According to different statistical reports, the prevalence of gait and balance disorders increases with age [12, 76]. Between the ages of 60 and 69 years, a prevalence of 10% is usually found, while a prevalence of more than 60% has been reported in people over 80 years of age [81]. Additionally, worldwide statistics state that the proportion of elderly people is increasing [82]. Specifically, it has been asserted that the number of people over 60 years will be larger than the number of children younger than 5 years by 2020 [19]. Moreover, population growth trends also establish that by 2050, the population over 60 will double from 900 million to 2 billion, from which the 80% will live in low- and middle-income countries [19, 82].

4.2.5 Other Conditions Natural and safe walking requires the proper and intact functionality of the following systems and features: locomotor control, balance, postural reflexes, sensory function and sensory-motor integration, the nervous system, the musculoskeletal system and the cardiovascular system. In this sense, there are several chronic and degenerative conditions that can also lead to disability and mobility impairments, such as: diabetes, coronary heart disease, heart failure, multiple sclerosis, arthritis, Parkinson’s disease, muscular dystrophy and Huntington’s disease [83, 84].

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Moreover, natural and safe gait also requires several cognitive and psychological functionalities, such as attention, executive functioning, perception, and memory [12]. Therefore, the presence of neurocognitive disorders, such as Alzheimer’s disease, dementia or mild cognitive impairment syndrome,2 has been reported to affect individuals’ mobility and autonomy [86]. Similarly, it has been identified that these disorders increase the risk of falling [85].

4.3 Biomechanics of the STS Task and Walking This section gives a description of the biomechanics of the sit to stand movement and walking. The purpose of this section is to understand the movement requirements that should be considered for the development of technical aids for assistance and rehabilitation.

4.3.1 Biomechanics of the Sit to Stand Task The STS movement has been studied by some researchers and characterized in different ways. In O’Sullivan and Schmitz [87] it is divided into two phases: pre-extension and extension. The pre-extension phase involves the horizontal translation of the body mass, and the extension corresponds to the vertical translation. The transition between two phases occurs when the person comes off the surface of the chair. In other work [88], the STS movement is described by a pair of phases termed flexion and extension. According to this work the STS movement is performed in 1.8 s. The flexion phase takes place approximately during the first 35% of the movement, and remaining 65% corresponds to the extension. In other works the STS movement is described in four phases [89, 90]. In Aissaoui and Dansereau [90] the phases are referred as: Forward momentum, Seat-unloading, Ascending and Stabilization. Forward momentum represents 27% of the STS cycle, and corresponds to the transfer of the weight from the seat to the feet area. Seatunloading represents 7% STS cycle and begins with a fast positive changing in vertical ground reaction forces, and vertical acceleration of the body mass. Ascending represents 39% STS cycle. It begins at the seat off, where the ground reaction forces reach its maximum. The vertical upward movement is ended when the knee is fully extended. Stabilization corresponds to the last 27% STS cycle. It is detected when the vertical ground reaction force fluctuation does not exceed ±1% of the body weight. There are two strategies to transfer from sitting to standing: momentum-transfer strategy (MTS) and zero-momentum strategy (ZMS) [87]. Both strategies consist in positioning of the center of mass within the base of support of the feet. Nevertheless, 2 Mild

cognitive impairment is a transitional state between normal aging and early dementia [85].

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the MTS involves a fast forward flexion of the trunk causing a forward momentum, then the extension movements are performed. In the case of the ZMS, first the trunk is flexed to put the center of mass within the region of the feet, once that position is achieved the vertical movement is performed. This strategy requires greater muscle force than in the case of MTS, however, ZMS is more stable. It is possible that people with muscle weakness use their arms to push off or get support. References [89] and [88] present the angular trajectories of the joints of lower limbs, hip, and trunk of a healthy person when performing the STS task. This information must be considered when reproducing the STS task using a rehabilitation device. After the STS movement, a person has to be able to stand and balance. Standing is a stable position with a high center of mass and a small support base, normally the body’s weight is equally distributed in both feet.

4.3.2 Biomechanics of Walking In general, locomotion is the ability to move from one place to another. Human locomotion, as all physical motions, may be analyzed kinematically, that deals with pure motion without reference to the masses or forces/moments involved in it, or kinetically, i.e. involving masses or forces/moments. Initially, some kinematical variables which are analyzed in gait analysis are presented. In gait, body actions are cyclic, in which the body is supported by one limb and then by the other. This sequence is defined by some parameters as stride and step. A stride, i.e. a locomotor cycle, is defined by the interval from one event on one limb until the same event on the same limb. Often, the first instant of foot contact defines the stride beginning. Additionally, a step is a portion from an event in one leg to the same in the opposite leg, therefore two steps are one stride [91–94]. Distance covered by one stride and stride quantity per minute, named stride length and rate respectively, are common gait linear kinematic parameters. These linear parameters have direct effect on locomotion velocity. Additionally, support and swing phase are linear parameters of gait cycle that indicates percentage of time for foot in soil contact and without it, respectively, during a complete cycle. Physical impaired individuals or special ground surface properties may cause significant variations in above parameters [93, 95]. Beside linear parameters, gait posses parameters that consider the angular motion between two consecutive members constrained by a joint. For instance, parameters as knee, hip and ankle angular position, velocity or acceleration during a stride are analyzed [96]. In addition to kinematical variables, there are some kinetic variables that are present in human locomotion analysis. In human locomotion movement, the individual is acted upon by the ground reaction force GRF at some time. This reaction force is exerted by the ground or surface upon which one is moving. GRF may be recorded through a force platform whose surface is coplanar to ground surface. In

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many occasions, body segments are individually analyzed, in this case the segment is separated from other segments at the joint. The net force acting in the separated segment across the joint is referred to as the joint reaction force. In general, this force magnitude is unknown, but it may be determined through kinematic, kinetic and anthropometric data [97–100]. Other useful kinetic variable to analyze human motion is the work-energy relationship. This index is used to determine the work done during several movements, nevertheless, the main use of this variable is in the area of locomotion. In locomotion, the internal work is calculated, i.e. the resulting total work done by all the body’s segments [101–104]. It is also important to consider the force distribution in a given area, named pressure. The concept of pressure is especially meaningful in motions in which a collision appears, as sole landing in walking and running. By using force platforms, it is possible to describe the path of the resultant GRF vector, called center of pressure COP [105, 106]. Summarizing, both, kinematic and kinetic variables, may be evaluated along time, step or stride period to indicate different gait behaviors. Researchers may use these results to analyze, evaluate, optimize and conclude different techniques, devices or technologies aiming at better results in health and sports that consider human locomotion. In the case of biomechanical evaluation of assistive or rehabilitation devices, it is a common approach to develop studies with non-pathological users to develop a base line for further comparisons with pathological users.

4.4 Traditional Rehabilitation Therapy This section describes some exercises used for the rehabilitation of STS task and walking. Before performing the exercises, the physiotherapist performs an evaluation of the patient [87]. This evaluation includes the review of the initial posture and environment, the execution of the movement (speed, direction, weight shift, vertical lift and balance), and the movement termination. Based on this evaluation, the patient’s difficulties are determined (difficulties due to the initial posture, lack of strength, asymetrical weightbearing, speed and/or proper coordination) and then, the way in which the exercises will be performed. These considerations must also be taken into account in case of using a rehabilitation robot. On the other hand, technology can be used to perform this kind of evaluation.

4.4.1 Sit to Stand and Balance Therapies During a rehabilitation therapy, several strategies are followed to rehabilitate a person to achieve the transfer from sitting to standing. Rehabilitation therapy consists in performing repetitive movements, usually, imitating the movement of a healthy

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person, i.e., moving the trunk forward, making the appropriate momentum transfer, then extension of the trunk and knees and finally being in standing position. The following consideration are recommended in [87]: • To perform the momentum transfer, the patient is guided by the therapist. First, the patient crosses his arms, and then the therapist pulls from the patient’s arms. In this way, the therapist supports part of the patient’s weight reducing the force that must be exerted on the hips to keep the upper part of the trunk extended. Alternatively, the patient can support his hands on a table with wheels (or other sliding object) that can be guided back and forth. Additionally, the therapist can help by applying a force to the knees or hips depending on the person’s disability. • When guiding the patient, the therapist must be in a suitable position in such a way he doesn’t block the patient moving forward. A repetitive practice of 11 to 14 times per day is recommended. • During the initial stages of rehabilitation patients are usually sitting on high surfaces, this is particularly useful in very weak patients, because a high surface helps to transfer the weight forward. Once the patient perform the STS task, he should be able to stand upright. There are different exercises for balance. The balance can be supported in active and resistive form. In the active form, the factors to consider are the ability to maintain correct alignment, and the ability to maintain the posture for prolonged periods of time. In the resistive form the patient is asked to stand stably while the therapist applies force in his trunk or pelvis in alternate directions (this is a proprioceptive neuromuscular facilitation technique). Other rehabilitation actions are weight shifts, steps forward and backward, and flexibility and stretching exercises.

4.4.2 Gait Therapy Gait rehabilitation includes the rehabilitation of every part of the lower limbs (including toes, foot, leg, thigh, and hip.) In the case of stroke, typical toe impairments include claw toe and hammer toe [107]. In the the claw toe, the toes are flexed and the tips are in contact with the ground, causing pain in the feet. Exercises for claw toe include extension and flexion of the toes, with the help of a therapists and active movements performed by the patient. In the same way to strengthen the muscles of the arch of the foot a force is applied in this part to flex the foot. Hammer toe is characterized by a sharp curve in the middle joint of the toes, and can be rigid or flexible, in case the rigid hammer toe tendon surgery may be required [108]. Weight shifts are basic exercises for balance and help to strengthen the lower limbs. Once the patient has enough strength, he can perform exercises with one leg, for this purpose, the patient can get support with his hands on a firm surface. In this exercises the patient is upright and standing in one leg; the free leg is extended and moves forward or backward trying to maintain the position for a few seconds.

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Subsequently, exercises where the hip’s joint of the free leg is flexed can be performed. On the other hand, bridging exercises strengthen hips and torso, and help to improve the stability. Technology based therapies for stroke patients are body-weight supported treadmill [109] and functional electrical stimulation [110]. Gait rehabilitation with bodyweight supported treadmill consists of a suspension system with a harness for the patient. This system permits weight shifting, balance, and stepping while walking is facilitated by a treadmill. In functional electrical stimulation small electrical charges are applied to the muscles through electrodes to stimulate movement. On the other hand, sarcopenia is one of the most important causes to loss of muscle power in older adults. Sarcopenia is a decrease in muscle strength, and the weakness associated with it, makes the elderly more likely to suffer physical disability, poor quality of life and mortality [111]. Therapies for sarcopenia include: Progressive resistance exercise, high-intensity progressive resistance exercise, aerobic exercise and plyometric exercise [112]. Progressive resistance exercise consist in performing dynamic or static contractions against a resistance; such as lifting weights, using weight machines or elastic bands. High-intensity progressive resistance exercise consists in performing a resistance exercise that is 70 to 89% of the maximum resistance the patient can bear for a single repetition of the exercise (such as lifting weights) [113]. Examples of aerobic exercise are walking, swimming, static bicycle ride, etc. Plyometric exercise consists in activities where the muscle-tendon unit is stretched and then shortened rapidly. Jumping is an example plyometric of exercise, this produces larger forces and velocities in a short period of time [114].

4.5 Sit to Stand Assistance and Therapy Robots Different prototypes and commercial products have been developed for the STS task, both for rehabilitation or assistance. The purpose of the rehabilitation devices is to generate movements similar to those of the STS movement of a healthy person, exerting forces on different parts of the patient’s body to perform this task repetitively. In assistance robots, the main objective is that the person achieves an upright position, either so that he can perform tasks at higher places in a mobile platform or can continue with the gait. Therapy robots can be fixed [115] or mobile platforms [116]. Assistance robots are usually mobile platforms. Different design of walkers with assistance for the STS task have been proposed in the literature [116–121]. Walkers with STS assistance can also be used for therapy. The wheels of the mobile platforms can be passive [117] or actuated [116], and therefore these device can provide passive or active assistance in walking. On the other hand, the designs of the STS rehabilitation robots are also characterized by the method in which the user is attached to or leaned on the robot. The user can be attached by his/her lower limbs [122], supported by a harnesses [123], leaned on his chest [116], or held by his arms [119]. Finally, in the case of the

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assistance robots, the user can get augmentative assistance [116](for persons with residual force) or complete assistance [124]. In Fattah et al. [115] the design of a passive gravity-balanced therapy device for sit-to-stand tasks is presented. The mechanism is a linkage that includes a four bar mechanism connected to the leg, thigh and torso by a link with a rotational joint, and a series of springs. In that work, a mathematical procedure is presented to determine the appropriate springs to make the total potential energy of the system constant during standing up. In Matjaˇci´c et al. [122] the STS trainer is introduced. It is intended to generate variable levels of mechanical support and speeds of STS transfer. This device consists in a deployable chair, the user is attached to the seat and backrest. The deployable chair is a one degree of freedom mechanism. In that work, a series of experiments were performed with a group of neurologically intact individuals. The authors assessed kinematics, kinetics and electromyography patterns of STS transfer in three experimental conditions with increasing degree of mechanical support. The STS-Care device is described in [125]. It is intended for the study and rehabilitation STS movement of post-stroke people. STS-Care is a three degrees of freedom parallel kinematic machine, made of six limbs (serial kinematic chains) connecting the base to a mobile platform. Parallel kinematic machines have several advantages compared to their serial counterparts, such as high accuracy, high stiffness, high payload and low inertia. The patient is supported by the mobile platform. The mechanism is controlled by a series of linear actuators (implemented by rotary actuators mounted on pulley-belt systems) in the base. The limbs connecting the moving platform to the base are compact to provide a clear view of all the markers placed on the patient’s body. Concepts of robotic devices that could be used for assistance and therapy of STS and walking are described in [123] and [126]. Liu et al. [123] presents the concept design of a mobile structure with an arm of two degrees of freedom for the rehabilitation of lower limbs. One of the purposes of the described design is the rehabilitation of the STS movement. The robot has wheels on the main support frame with the ability to move during walking. The arm is designed for various modes of rehabilitation workouts (bed, STS movement and gait training). The described structure is designed with a height ranged from 150 to 190 cm and use two linear motors to control the arm. In Carrera et al. [126] the concept of a device to assist the user in standing up, sitting down, and carrying out locomotion tasks is presented. The robot is composed of three links connected by three joints. They are a traction system on the rails, a rotational joint with vertical axis, and the actuated prismatic joint that moves up and down the arm support. The rail traction system is located on the ceiling and supports the entire structure. Those rails are located according to a predefined route in the house. The rails can be curved and in some cases, a special mechanism can be used for making rail changes with turntables and exchangers. The main advantage of this concept is its inherent structural rigidity. In Chugo [116] a device for assistance and rehabilitation of movements to stand up and walking is presented. The system consists of a support pad with three degrees of freedom hybrid mechanism (serial-parallel kinematic chain) and an active walker

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system. The lifting mechanism is actuated by servomotors that are connected to worm gears, thus, it can maintain its posture even if system power is down. The mobile platform is actuated by two brushless motors on each front wheel. These motors can operate with traction force limitation and can follow when the patient pushes the walker against to advance in a given direction by a pair of force sensors. Both wheels also have electromagnetic brakes. Electromagnetic brakes can stop the walker when the patient seems to fall down. The prototype can lift up the patient of 1.8 m height and 150 kg weight maximum. The user is supported by his chest on the pad during the STS task. Other similiar devices are presented in [121] and [118]. Other design of a walker with STS assistance is presented in [117]. The structure has four castor wheels, so the user can walk once he is upright. A harness is used to lift the user. The standing up is achieved with a four bar mechanism activated with a pneumatic actuator.The pneumatic system consist of two cylinders and a conventional bike pump. In Salah et al. [119] the design of the EJAD device is described. According to the authors of that paper the design of this device imitates the caregiver’s motion during the support task. The user is held by his arms and back. The active walker system is a differential drive mobile platform with a pair of actuated wheels and a pair of castor wheels. On the other hand, in [127] the use of the Ballbot to assist a person to stand up is presented. Ballbot is a mobile platform that balances on a single spherical wheel. In the described experiments the user is pulled by his hands. Complete assistance robots have been designed for people with disabilities that have no chance of recovery. These devices usually allow people to change postures and perform activities of daily living with free hands (e.g. washing their hands, washing dishes, cooking, taking objects from countertop and cabinets, etc. Automated standing machines have a harness for lifting a person and stand on a platform with wheels. There are more developed systems that include actuated mobile platforms with STS assistance. Among them are the Tek Robotic Mobilization Device and QOLO. The Tek Robotic Mobilization Device robot is commercially available, the robot is a four-wheel mobile platform that supports disabled people to lift and sit them as well as move them in a standing position [124]. The user operates the device using a joystick in upright stance. QOLO [128] is a mobile device, in this case it consists of a deployable chair, the user is attached by his lower limbs. As a matter of fact, that this device does not have a frontal component, the user is capable of performing tasks with his hands.

4.6 Gait Assistance and Therapy Robots With the rapid advances of technology in the last decades, several devices and strategies have been developed to improve gait training interventions. These devices are mainly aimed at enhancing patient’s experience during the rehabilitation process, and providing safe, intensive and task oriented therapies [129].

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In general, the integration of technology and robotics to rehabilitation devices enables the following characteristics: (1) Precise and repeatable movement patterns and therapies, (2) intensive activities with programmable and measurable difficulty, (3) online measurement of the performance and physiological state of the patient, (4) motivating and engaging rehabilitation environments through the use of virtual and augmented reality, as well as feedback strategies, (5) reliable assessment of the patient’s rehabilitation progress, (6) reduction of the physical effort of the therapists [130–132]. According to the literature evidence, gait rehabilitation devices and technologies can be classified into different categories based on their behaviour, principle of operation, required assistance, rehabilitation stage, among others [15, 130, 133]. Based on the classification proposed by Martins et al., robotic alternative devices and robotic augmentative devices can be found. Specifically, alternative devices are used when complete assistance is required, and augmentative devices are used when patients exhibit residual locomotion capacities [133]. Among robotic alternative devices are robotic wheelchairs and autonomous vehicles. Robotic augmentative devices include treadmill-based or stationary gait trainers, ambulatory training devices, wearable devices and smart walkers. Robotic wheelchairs are equipped with actuators, sensory interfaces and advance processing algorithms to provide easier and safer navigation [134]. Moreover, considering the patient’s requirements, the robotic wheelchairs may include multimodal input interfaces, such as joysticks, voice recognition modules, image processing systems, and bio-signals monitoring modules (e.g., EMG and electroencephalography (EEG)) [134]. Autonomous vehicles for assistance include robotic scooters and bipedestation vehicles [133]. These devices include several actuators and multimodal user interfaces to allow intuitive control and interaction. Moreover, these devices are commonly equipped with lifting mechanisms to provide sit-to-stand capabilities [135]. Robotic augmentative devices exploit the residual locomotion capacities of the patient to provide safe, intensive, repetitive and task-oriented rehabilitation interventions [130]. Therefore, the patient has an active role in the rehabilitation tasks [15, 130]. Moreover, these devices are generally equipped with control approaches aimed at: (1) allowing a margin of error in patient’s performance without providing assistance, (2) triggering assistance in relation to the amount of exerted force or velocity (i.e., intentions of movement), (3) enabling joint compliance, and (4) disabling robotic assistance under specific scenarios [129, 130]. Robotic treadmill-based systems consists on using a suspension system to provide symmetrical removal of patients’ partial body weight, while a robotic device moves their lower limbs [130]. These devices constitute the most common method for mobility training and are mainly aimed at improving functional movements and sensory stimulation through repetition [15, 133]. Several well known and commercial devices are found in literature such as, Lokomat [136], Lokohelp [137], LOPES [138], G-EO [139], ALEX [140], among others [15, 133, 141]. On the other hand, ambulatory training devices use a body weight support system to provide over ground training but the treadmills are not used [133]. In these devices,

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the support system allows overground walking, while the user is being assisted by a robotic device. These devices are able to provide dynamic assistance, in such a way that the patient learns to walk with proper posture [133]. The category of wearable devices includes active orthoses (i.e., also referred as exoskeletons) and active prostheses are commonly found. These devices are carried by the user either to improve function of movable parts of the body or to substitute a lost member [133]. By means of different types of actuators and sensory interfaces, these devices mechanically compensate and improve the functionality of the affected joints [133, 140]. In this sense, these devices allow the patient to complete a wide range of daily living activities such as walking, standing, sitting, as well as going upand downstairs [140, 142]. Regarding the control strategies, these devices are usually based on the assist-as-needed control concept, where the active motion of the patient is encouraged [130]. To this end, the control strategies seek to activate efferent motor pathways and afferent sensory pathways during training [130]. Among the most relevant and notable devices, HAL exoskeleton [143], Ekso Bionics exoskeleton [144], Phoenix [144], MyoSuit [145], Exo H3 [146], ReWalk [147], BLEEX [148], RoboKnee [149], AGoRA exoskeleton [150] and T-Flex [151] are found. Smart walkers are mechanical structures with three or more wheels that assist in gait to people with walking difficulties. The main function of these devices is to provide balance and stability to the user, acting mainly as support or supporting the weight of the user. They can be used as rehabilitation or assistance robots [152]. One or more wheels can be actuated to drive the smart walker. These devices could include a navigation system to determine a trajectory avoiding obstacles [153], and in this way assist people with sensory or cognitive disabilities. They also allow gait monitoring to determine user status and control forward speed [154, 155]. Smart walkers provide two types of physical assistance: passive and active [133]. Passive assistance is provided by the structure that supports the user. Users hold the smart walkers with their hands [156] or by means of their forearms, and other designs support users by the waist and back [152]. Active assistance consists in the control of the servomotors of the wheels and the components that support the user. Usually, the actuators on these devices can control brakes in the wheels [157], or provide the necessary traction force to move the device in a given direction [158] or to compensate gravity on inclined grounds [156]. These devices are equipped with various sensors to perform navigation, including sonar, laser/infrared proximity sensors and bumper switches [157, 159]. The purpose of this class of sensors is to prevent collisions with the environment. When an obstacle is detected, the system drives the smart walker in a different direction, or produce a sound, a vibration, or force feedback signal [160] to indicate the presence of such obstacle [161]. In addition, a navigation system may have pre-programmed routes to take the user to different places of interest [162]. On the other hand, in [4] several of human robot interaction strategies for walker-assisted locomotion are presented.

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4.7 Conclusions Rehabilitation robotics has become a very attractive research area with huge potential to improve the quality of life of patients. Recent advances in this technology show that it may be part of the solution to the difficulties faced by a large segment of the population due to disability. In this chapter, different topics related to the application of this technology for assistance and rehabilitation of patients with lower limb disabilities were discussed. In the first part, pathologies related to motor disabilities were described in order to contextualize the reader about what conditions are susceptible to be benefited with the implementation of assistance and/or rehabilitation robots. Consequently, several robotic tools are presented, particularly focusing on the ones with clinical evidences or huge potential in this field. Additionally, a description of the biomechanics of STS tasks and walking was made. In this context, motion patterns from healthy subjects are implemented in two ways: firstly, defining motion trajectories to be performed by robotic devices for assisting or retraining pathological users, and secondly, healthy patterns enable the clinicians to compare patients’ kinematics for following up the rehabilitation treatments. Finally, a literature review of assistance and robots for therapy for both, sit to stand task and walking is presented. In general terms, two types of therapeutic interventions can be distinguished in patients with mobility impairments. On the one hand, if the patient exhibits residual locomotion capacities after a neurological accident, it is possible to implement a rehabilitation process. This type of interventions are mainly based on the principle of cerebral plasticity. Specifically, the intensive, repetitive and focused training might induce a cortical reorganization, and therefore the recovery of motor and sensory functions. In this sense, neurological patients such as those who suffered a stroke, cerebral palsy or spinal cord injury could benefit from rehabilitation processes. On the other hand, if the patient’s disability is given by a process of progressive degeneration of the locomotor system, the cardiovascular system or the neurological system, it can be established that the patient could benefit from assistive devices. Examples include older adults, patients with musculoskeletal impairments and patients with cognitive impairment. Summarizing, several robotic platforms have been developed for rehabilitation and assistance of the gestures of sit to stand task and walking. Those robotics tools can be alternative or augmentative devices. Specifically, alternative devices are used when complete assistance is required, and augmentative devices are used when patients exhibit residual locomotion capacities. Among robotic alternative devices are robotic wheelchairs and autonomous vehicles. Robotic augmentative devices include treadmill-based or stationary gait trainers, ambulatory training devices, wearable devices and smart walkers. Finally, the authors would like to point out two topics as future works, firstly, the need of developing a benchmarking study to evaluate and compare several mechanical architectures available in the literature, and secondly, performing an analysis to determine whether it is possible to develop general-purpose devices for therapeutic and assistive procedures that involve the functionality of the lower limbs. Indeed, the development robotic devices for rehabilitation and assis-

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tance for STS task and walking is still a matter of study and has a huge potential in both, clinical and home settings. Acknowledgements This work was supported by PRODEP Mexico, the Colombian administrative department of science, technology and innovation Colciencias (grant ID No. 801-2017), CYTED research network REASISTE (grant 216RT0505).

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Chapter 5

Adaptable Robotic Platform for Gait Rehabilitation and Assistance: Design Concepts and Applications Sergio Sierra, Luis Arciniegas, Felipe Ballen-Moreno, Daniel Gomez-Vargas, Marcela Munera, and Carlos A. Cifuentes

5.1 Introduction Physical rehabilitation is mainly aimed at restoring people’s movement and functioning affected by injury, illness or disability [1]. Considering the health condition of each patient, gait rehabilitation and assistance therapies focus on providing, compensating, increasing or re-training the lost locomotion capacities, as well as the cognitive abilities of the individual [2]. Specifically, training interventions seek to improve walking performance by: (1) eliciting voluntary muscular activation in lower limbs, (2) increasing muscle strength and coordination, (3) recovering walking speed and endurance (i.e., usually accompanied with cardiovascular training), and (4) maximizing lower limbs range of motion [3].

S. Sierra (B) · L. Arciniegas · F. Ballen-Moreno · D. Gomez-Vargas · M. Munera · C. A. Cifuentes Colombian School of Engineering Julio Garavito, Autopista Norte AK 45 No. 205-59, 111166 Bogota, Colombia e-mail: [email protected] L. Arciniegas e-mail: [email protected] F. Ballen-Moreno e-mail: [email protected] D. Gomez-Vargas e-mail: [email protected] M. Munera e-mail: [email protected] C. A. Cifuentes e-mail: [email protected]

© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 M. Cardona et al., Exoskeleton Robots for Rehabilitation and Healthcare Devices, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-15-4732-4_5

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To empower and maximize the effects of the physical rehabilitation therapies, several devices have been developed to assist and support mobility. Concretely, mobility assistive devices are aimed at overcoming and compensating physical limitations by maintaining or improving individual’s functioning and independence in both clinical and everyday scenarios [4]. Among these devices, walker-based devices hold a great rehabilitation potential [5]. In order to rehabilitate normal lower-limb function as much as possible, gait rehabilitation such as locomotion training is commonly employed as therapy. The rehabilitation process commonly employs physical therapy in conjunction with robotic devices and rehabilitation machines. In this context, The AGoRA (Development of an Adaptable Robotic Platform for Gait Rehabilitation and Assistance) Project (Colciencias 801-2017) is focused on the development on a novel and affordable robotic platform to provide physical and cognitive support in rehabilitation scenarios. This project studies the development and integration of modular and active orthoses, an example of this robotic solution is the T-FLEX exoskeleton for ankle assistance and rehabilitation (See Fig. 5.1), which configure a biomechatronic exoskeletons combining AI approaches for gait phases detection, soft robotics to naturally empower human joints and variable impedance controllers looking at promoting natural physical interaction. These devices provide external controlled power to the impaired joints to compensate for walking function. In that way, this robotic platform may be used to substitute or enhance motor function by driving the user’s joints through a functional walking pattern. Furthermore, the AGoRA project presents a novel combination of a lowerlimb exoskeleton with a Smart Walker (SW) (See Fig. 5.2), which presents potential benefits for mobility assistance and gait rehabilitation. This platform intends to represent a breakthrough in terms of developing close interactions with human beings, Fig. 5.1 Actuator for the ankle joint in the AGoRA exoskeleton. T-FLEX integrates concepts of bioinspiration, soft robotics, and variable stiffness in a device. It assists the dorsi-plantarflexion movements without restricting the other planes of motion

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Support System

Handlebars

Hip Free Joint Environment Sensing

Hip Active Joint

Force Sensors

Control Units

User Sensing

Knee Active Joint Physical Interfaces

Motorized Wheels Exoskeleton

Smart Walker

Fig. 5.2 Illustration of the AGoRA Exoskeleton and the AGoRA Walker representing sensing and actuation systems

such as: patients, clinicians and social environments. In that way, this book chapter presents the ground bases of the AGoRA Project and some examples performing trials with non-pathological and pathological users.

5.2 Biomechatronic Design Nowadays, several types of robotic devices, such as smart walkers, active joint orthosis, as well as lower or upper body exoskeletons [5–7], have been developed to provide rehabilitation, assistance or augmented physical capabilities in different scenarios (i.e. clinical, industry and military) [8, 9]. Most of these devices are focused on rehabilitation to provide physical and cognitive support to people with mobility impairments. To this end, the process of designing and deploying a customized solution is often referred as biomechatronic design. This process seeks to adjust a biological model of the human-robot interaction in order to identify the functions and technical requirements creating a rehabilitation device. Bearing in mind the above mentioned, the biomechatronic design process is composed by five main steps related to: (1) targeted activity selection, (2) user’s features definition, (3) mechanical structure and actuators selection, (4) physical interfaces design and (5) physical interaction assessment [10]. To describe this process, several concepts related to wearable devices (i.e., exoskeletons and orthoses) will be used. However, the above steps can be easily extrapolated to the design of any rehabilitation device. Figure 5.3 outlines the relationship between the steps and the targeted activity that the device should support.

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Fig. 5.3 Diagram of the biomechatronic design process. Each box represents one step within the process and their relationships between them

5.2.1 Targeted Activity In the particular context of rehabilitation and assistance, these devices are focused on populations that suffered from work-related accidents or neurological injuries (i.e. stroke, cerebral palsy, spinal cord injury). In this sense, the biomechatronic design process considers the definition of the anthropometric measurements of the user, as well as the specification of the therapeutic or assistive objectives. Moreover, the design process should also consider the key activities of daily living required by the patient, such as walking, standing-up, grasping objects, climbing stairs, among others [11]. Depending on the specific activity that the rehabilitation device should support, the amount of required power, range of motion (RoM), maximum torque, among others factors could vary [12]. The selection of the activity to be assisted affects the biomechatronic design process transversely. In particular, this factor not only determines the kinematics of the user, but also the mechanical, energy and interaction characteristics of the rehabilitation device.

5.2.2 Human Features Regarding the human features, it is paramount that the design process is usercentered. On the one hand, the kinematic and kinetic behavior of the user during the targeted activity should be considered, in such a way that the mechanical structure and the actuators capabilities are properly selected. On the other hand, the anthropometric measures are the second part related to the human features in accordance

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Fig. 5.4 Shoulder joint example to create the device’s kinematic chain. Among the shaded area (just illustrative) an arragement of one-DOF joints could perform, as close as possible, the shoulder kinematics

with the device’s target population (i.e. children, adults, and elderly people). Specifically, the target population limits the kinematic and kinetic behavior, as well as the alignment between the actuators and the assisted joints [13]. Moreover, this step is also focused on the human joints. Essentially, the behavior of the joints is determined by their anatomical composition and the function they perform in the different patterns of movements. In fact, the biological behavior of most of the human joints is complex due to their intrinsic geometry [14]. Besides, human joints are composed by multiple bones, tendons and muscles that work together to perform an specific motion. Thus, the joints often exhibit several degrees of freedom (DOF) and increased complexity, due to the involved anatomical elements [15]. Accordingly, to properly comprehend the motion generated by human joints, they could be modeled as an arrangement of theoretical joints. In this sense, the definition of each joint could be simplified into 1-DOF joints, such as revolute or prismatic, and their configuration could be defined as series or parallel [16]. Joint analysis through theoretical joints is widely used in several devices, and the set of joints types and configurations is often referred as the device’s kinematic chain (See Fig. 5.4) [14, 17, 18].

5.2.3 Mechanical Structure and Actuators Once the human features are analyzed, the definition of the kinematic chain of the device provides rough approximations of the required mechanical structure. In general terms, there are two design approaches that allow the device to follow the user’s movement pattern. On the one hand, the device could be designed as an Antropomophic Structure (AS), in which the structure’s shape follows the assisted limb by placing the actuators near to the user joints. This approach is the most commonly used

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in active lower-limb exoskeletons, such as HAL-3, BLEEX, and ALEX [19–21]. In contrast, Nonantropomophic Structures (NS) differs from the human shape extending the design possibilities. Although, these structures provide several mechanical advantages (i.e. backdrivability, proper distribution of masses), the complexity of the design process may increase [22]. In addition to the above, structure design has to consider the type of actuator to be coupled (i.e. pneumatic actuator, hydraulic actuator, motor drive, series elastic actuator, variable stiffness actuator) [23]. In essence, the mechanical principle required to transmit movement or energy to the assisted joint could change according to the targeted activity and device’s purpose (i.e., rehabilitation, assistance, physical augmentation). For instance, lower-limb exoskeleton’s response features (i.e. device bandwidth, weight, power) are affected by the type of actuator, as well as the relation between the supplied torque to the user and device’s weight [24]. Therefore, more robust or simpler actuators may be suitable for different scenarios.

5.2.4 Physical Interfaces To ensure the transmission of energy from the actuator to the user, the physical interface, between the user and the mechanical structure, is the most important feature that affects exoskeleton’s performance [25]. To generate motion, the torques and forces produced by the exoskeleton joints have to be transferred to human joints as best as possible [9, 26]. Therefore, to achieve an optimal performance, the biomechatronic design has to consider several issues: (1) misalignment within the joint exoskeleton and the user joint, (2) variability between subjects, and (3) actuator capabilities and it’s location [27]. As previously explained, the biological behavior of the human joints is characterized by complex motions that allow several tasks to performed. More deeply, one of the main reasons for this complexity is that the biological swivel is not configured a single point. Therefore, an important misalignment is generated between the exoskeleton and the user (See Fig. 5.5). In other words, the rotation axis of the user joints and the exoskeleton joints are mismatched [28]. In this sense, to ensure the transmission, a mechanical analysis of the interface must be performed aiming to reduce the misalignment and increase the mechanical efficiency. Moreover, the mechanical behavior depends on the geometry used to couple the limb to the exoskeleton. Usually, the interface has a complex geometry because of the limb’s surface and variability within users.

5.2.5 Physical Interaction Among this section, biomechatronic design have been addressed to understand the human-activity interaction. Defining different human features given design criteria to

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Fig. 5.5 Example of knee misalignment along the sagittal plane

choose a type of structure (i.e. either AS or NS) and actuator (i.e. pneumatic actuator, hydraulic actuator, motor drive, series elastic actuator, variable stiffness actuator). Moreover, physical interfaces ensures energy transmission to the user avoiding misalignment between the device and the user. However, monitoring the user and device is also crucial to warrant the safety during a rehabilitation task. In this sense, the assessment of the physical interaction considers the user intentions and device’s reactions through sensors placed among the device. Next section further describes and explains this topic. Biomechatronic design process allows to create rehabilitation devices. Take into account one or more targeted activities and its design is based on human features. It also consider the interaction between the user and the device.

5.3 Sensor Interfaces for Gait Phase Detection and Physical Interaction in a Lower Limb Exoskeleton Human-Robot Interaction (HRI) is an important factor to ensure user’s comfort, safety and a reduced energy consumption during assisted gait[29]. In this sense, assistive devices must be equipped with sensory interfaces that allow them to estimate the physical interaction and movement intentions of the user. Additionally, given the particular capacity of the lower limb exoskeletons to independently actuate user’s joints, it is important to properly estimate the user’s gait phases to adjust the control strategies to be implemented [24, 30].

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Regarding the sensors, they can be whether worn by the user or installed in the structure of the device. Specifically, the AGoRA exoskeleton is designed in such a way that there are no sensors physically attached to the user’s skin. Therefore, the exoskeleton is equipped with two types of sensors: kinematic and kinetic. Kinematic sensors are used for measuring hip and knee angular position, and foot angular velocity, whilst kinetic sensors measure the interaction force between the user’s limbs and the mechanical structure of the exoskeleton. The exoskeleton joints are driven by DC motors, which are equipped with internal relative incremental encoder (i.e., used for the implementation of position and impedance controllers). Similarly, the exoskeleton has an absolute magnetic encoder placed concentrically to each joint assembly (See Fig. 5.6). This absolute rotary encoder produces a unique digital code for each position of the motor shaft, whereas voltage outputs of the motor encoders proportionally vary according to the joint angular displacement. These measurements are given in terms of increments. Employing this information, the angular position of both hip and knee joints are monitored and used as control parameters. Additionally, the motor drivers are able to measure motor velocity and position by means of embedded incremental encoders and hall effect sensors.

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Fig. 5.6 Sensor interfaces of the AGoRA exoskeleton. Dashed lines depict the components of both hip and knee modules. Additionally, an inertial measurement unit (IMU) and a custom shoe insole based on force sensitive resistors (FSR) are located at the user’s assisted foot for the purpose of gait phase detection

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5.3.1 Gait Phase Detection As mentioned above, to properly control motion patterns in lower limb rehabilitation devices, it is necessary to accurately detect gait phases. To this end, a custom shoe insole instrumented with Force Sensitive Resistors (FSR), and an Inertial Measurement Unit (IMU) are placed on the dorsal side of the foot (See Fig. 5.6). By using these sensors, the user’s gait cycle can be partitioned into known gait events, namely Heel Strike (HS), Flat Foot (FF), Heel-Off (HO), and Toe-Off (TO). In particular, gait phase detection methods have been used in robotic lower-limb orthoses to command force-field behaviors in dependence to the detected gait sub-phase. For instance, during initial ground contact an active ankle-foot orthosis should guarantee smooth landing for stroke survivors suffering from drop foot [31].

5.3.2 Sensors for Physical Interaction As mentioned in Sect. 5.2, robotic orthoses as exoskeletons are physically attached to the patient’s impaired limbs. Therefore, these devices rely on several strategies to communicate with the patient. This communication is often focused on different aspects such as safety [32], kinematic compatibility with the patient [33], and movement assistance. This type of interaction is often referred to as physical Human-Robot Interaction (pHRI). To achieve natural and compliant pHRI, the exoskeleton must be able to allocate force and torque profiles to the user’s joints in a controlled way [15]. Moreover, since user’s joints are often modelled as a combination of theoretical joints, the exoskeleton should provide support and allow movement along the sagittal plane. Particularly, the AGoRA exoskeleton was designed to provide free abduction-adduction hip movements. Another important issue for natural and proper pHRI is determined by the fact that the device can be moved freely by the user [34]. To this end, the device should exhibit low impedance and allow unconstrained movements at subject’s voluntary command [30]. Thus, the device should follow the patient’s movements without applying any significant forces [35, 36]. This concept is often referred to as backdrivability, ensuring reliability and compliance. In this sense, literature suggest the implementation of the assist-as-needed (AAN) concept, where dynamic forces/torque behaviours are applied at each joint depending on the level of assistance required to perform different tasks [37, 38]. This strategy promotes the active participation of the patients during therapy, considering their degree of disability [30]. Regarding the estimation of the interaction force/torque between users and rehabilitation devices, resistive sensors have been widely used in lower limb prostheses and exoskeletons [39, 40]. Particularly, the AGoRA exoskeleton uses strain gauges to estimate the applied force by the patient at each link of the mechanical structure. These sensors are based on the measurement of the deflection caused by the force

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Fig. 5.7 Description of the force/torque acquisition process through strain gauges: (1) Conversion of force/torque to a voltage value, (2) condition of the voltage signal, (3) analog-to-digital Conversion (4) force/torque estimation through the characterization of the strain gauges

applied at each link, and output a proportional voltage through a half Wheatstone bridge. To characterize the relationship between the output voltage and the applied force/torque, a linear variation of known force/torque values is performed. Figure 5.7 describes the force/torque acquisition process. Specifically, the process is composed by 4 main steps: (1) the deflection of the material is measured and converted into a voltage value by the strain gauges, (2) the resulting signal is amplified so that the voltage variations can be easily recognized, (3) an Analog-to-Digital (A/D) conversion is performed, (4) and the characterization is used to estimate the equivalent force/torque applied by the user.

5.4 Variable-Impedance Controllers for Hip–Knee Robotic Exoskeletons Starting from the measurement and identification of the pHRI between subjects and a robotic exoskeleton, proper and efficient control strategies should be implemented to ensure device’s compliance and transparency. As reported in literature, these strategies are often deployed by means of impedance controllers [30]. These controllers model the robotic device as dynamic system and allow the modification of stiffness, damping, and inertia parameters, in order to vary the amount of assistance provided to the patient [41]. Moreover, several studies have found that the implementation of variable impedance strategies might be comparable to the assistance provided by physical therapists [30, 41, 42]. Particularly, the AGoRA exoskeleton applies a similar strategy by modelling its mechanical structure, as manipulator robot with 2 revolute joints located at hip and knee joints [16, 43]. On the one hand, the dynamics of the robot are modelled with feedforwad controller. This strategy is aimed at considering intrinsic parameters of the structure, such as moments of inertia, centripetal forces, gravity and friction forces [44]. On the other hand, to consider the pHRI a feedback controller is used [36, 45, 46]. As proposed by Hogan in 1984 [47], this controller models the device

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as mass-spring-damper dynamic system, where the position and angular velocity of each joint are evaluated. Equation 5.1 describes such strategies: Feed f or war d

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where M is the inertia matrix, C defines the centripetal forces, F considers the friction forces and G is the gravity expressed as a torque. Moreover, q corresponds to the angular position, q˙ is the angular velocity, and q¨ is the angular acceleration. Finally, k corresponds to the spring’s elasticity constant and B to the damping constant. Regarding the feedforward parameters, an accurate characterization of the mechanical structure and hardware of the exoskeleton is required. Moreover, to illustrate the feedback concept, Fig. 5.8 depicts the dynamic modelling of the exoskeleton. In this way, the control loop is closed by the interaction with the subject. Specifically, the impedance controller takes into account the angular position error q, and sets a proportional control action given by the spring’s elasticity constant k. Likewise, the angular velocity error q˙ is multiplied by the damping constant B [48–50]. Depending on the specific task that the robotic device should, the controller gains could be adjusted to provide the required assistance. For instance, assisting sit-tostand movement might require the device to be virtually more stiff than when assisting a walking pattern. Due to the rehabilitation capabilities of the lower limb exoskeletons, several studies report their applicability in gait training therapies [30, 40, 51]. Specifically, people with mobility impairments might exhibit reduced motor control, as well as decreased stability and support in the lower limbs [52]. Therefore, implementing impedance controllers with variable dynamic parameters along with the AAN concept, constitute a promising solution to assist the subject at the different gait phases [36]. As

Fig. 5.8 Impedance controller as a mass spring damper system applied in each joint of the lower limb exoskeleton Damper Spring

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mentioned in Sect. 5.3, employing sensors, such as IMUs, gait sub-phases can be detected [30]. Thus, once the gait sub-phase is identified, the default controller constants can be proportionally changed [53]. Considering that the AGoRA exoskeleton is able to detect four gait sub-phases of the run, Eq. 5.2 describes the adaptation of the controller constants: Mi = M d · G i , 1 ≤ i ≤ 4 Di = Dd · G i , 1 ≤ i ≤ 4

(5.2)

where Mi corresponds to the moment of inertia, Di to the damping constant, Md is the default inertia matrix of the system and Dd is the default damping constant of the system. Moreover, G i refers to the proportional gain applied according to the specific sub-phase i. Similarly, the initial parameters Md and Dd are defined considering anthropometric measurements of the subjects and their level of disability. In this sense, the variable behavior of the system’s stiffness, as well as the AAN concept during walking, increases and promotes the patient’s active participation during therapy.

5.5 Actuation Systems and Control for Ankle Rehabilitation The human ankle is a complex joint that plays a fundamental role in the execution of gait and other activities of daily living (ADL) [54]. In terms of energetic cost, the ankle should be able to redirect the center of mass within gait phase transitions [55]. Therefore, the key functionalities are intended to allow the foot to perform a gradual contact with the ground, to move the body weight, as well as to provide a propulsive force to initiate the leg swing [54]. The main movements of the ankle joint are plantardorsiflexion, ab-adduction, and inversion-eversion. However, combinations between these movements create three-dimensional motions such as supination and pronation [54]. The proper control of the ankle movements results in a fundamental factor for balance and stability [56]. The reduction of the ankle movements due to the neurological conditions can induce abnormal gait patterns. Patients often lack some phases of the gait cycle and consequently have to perform compensatory movements in the hip and knee joints [57]. In the kinematics context, there is a reduction in the subject’s walking speed, stride length, and cadence [58]. On the one hand, the limited foot clearance caused by this alteration is related to a high probability of falls [59]. On the other hand, the metabolic cost is negatively affected [60], increasing the risk for injuries and permanent damages in the locomotor system. Robotic solutions, as Active Ankle Foot Orthoses (AAFOs), have been developed both to counteract the effects aforementioned, as well as to support rehabilitation scenarios. In this way, the inclusion of these devices is looks forward to accelerate

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the rehabilitation processes, to provide and re-train the lost movements, and hence to improve the patients’ life quality [61]. An example of this robotic solution is the T-FLEX exoskeleton (See Fig. 5.1), which is part of the AGoRA project.

5.5.1 Actuation Systems Implemented on AAFOs The state-of-art classifies into three main categories the actuation principles applied in AAFOs: Stiff Actuators (StAs), Serial Elastic Actuators (SEAs), and Pneumatic Actuators (PnAs) [62]. Some authors include other less common principles used as actuation mechanisms in AAFOs, such as Hydraulic Actuators (HyAs) and Magnetorheological Actuators (MRAs) [63]. However, other actuation systems widely implemented nowadays can be categorized as, Variable Stiffness Actuators (VSAs) and Cable-Driven Actuators (CDAs) [64].

5.5.2 Control Strategies for AAFOs Once the actuation system is selected, a proper control strategy should be designed. Specifically, a control system must guarantee a proper pHRI in terms of the overall safety, transparency, and level of patient participation [65]. Additionally, it should regulate the level of forces and motions applied to the joint under rehabilitation processes [66]. Taking into account the main factors aforementioned, the literature classifies the control strategies into two groups: (1) low-level and (2) high-level [67].

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Low-Level Strategies

Low-level refers to basic implemented strategies for controlling actuators such as position, force, admittance, and impedance controllers [67]. These strategies support more elaborate control architectures focused on real applications with human interaction named as high-level. The selection of a low-level strategy will depend on the type of actuator and the feedback acquired by the sensors estimating the HRI. Moreover, the control law could be given by different techniques applied on linear and non-linear systems (i.e., fuzzy logic, PID and its variations, adaptive control, controllers based on neural networks and others) [61].

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High-Level Strategies

High-level control strategies are algorithms designed explicitly to promote ankle plasticity [68]. These strategies are classified depending on the level of pHRI and

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the effort performed by the user. These groups include passive controllers, active controllers, active-assistive controllers, and active-resistive controllers. • Passive Controllers: In this type of control, the device performs motions of the ankle joint, and no effort is required from the user. • Active Controllers: This strategy requires voluntary motion by the user to a prespecified target while the device records kinematic and kinetic parameters. • Active-assistive Controllers: For this type of controller, there is a cooperation between the device and the user for carrying out a task. Initially, the system has the same function as an active controller, but if the user require assistance, the device will provide the support needed to achieve the goal. • Active-resistive Controllers: This controller is intended to be a trainer. To this end, the device generates an opposing force that resists to the user’s movement. On the other hand, the inclusion of sensors in ankle devices can in turn divide the control strategies into the following categories: (1) proportional myoelectric controller, (2) adaptive gain proportional myoelectric controller, (3) phase-based controller, and (4) push-button controller [62]. The proportional and adaptive myoelectric controllers are related to the acquisition and processing of muscle activity. Conversely, the phase-based controller is the most common strategy used in rehabilitation and assistance scenarios [62]. In this type of controller, the control action depends on the detection of gait events. For this purpose, wearable sensors such as IMUs and FSR mentioned in Sect. 5.3 has been implemented. Finally, in the pushbutton controller, the actuation is proportional to the displacement of a push-button. This strategy leads to controlled tele-operation by the user.

5.6 Smart Walkers In general terms, the walkers improve overall balance by increasing the patient’s base of support, enhancing lateral stability, and providing weight bearing during bipedestation [69, 70]. Similarly, the walkers use the patient’s remaining locomotion capability to move, and thus, they are usually prescribed for patients in need of gait assistance during functional daily living tasks [71]. Additionally, evidence shows that walker-assisted gait is often related to important psychological benefits, including increased confidence and safety perception during ambulation [71, 72]. Although conventional walkers are widely used for walking assistance, there are several factors that limit their application in rehabilitation settings and more complex scenarios. On the one hand, several studies have reported that these devices do not ensure enough safety during walking, since the risk of falling is quite high [73, 74]. On the other hand, conventional walkers do not provide proper physical and cognitive support to patients with higher assistance requirements [5, 71, 72].

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For instance, patients with visual limitations may require assistance for safe navigation or may require to be guided in different scenarios. Similarly, patients with reduced muscle capacity may require active assistance from the device when walking [71, 75]. According to the above, the possibility of improving and enhancing the capabilities and functionalities of the conventional walkers through the inclusion of robotic technology has arisen. Specifically, the Smart Walkers (SWs) usually include sensory interfaces and actuation systems to provide more efficient and robust gait rehabilitation. Broadly speaking, the integration of technology and robotics enables the following characteristics: (1) Precise and repeatable tasks, (2) intensive activities with programmable and measurable difficulty, (3) online measurement of the performance and physiological state of the patient, (4) more engaging rehabilitation environments through the use of virtual and augmented reality, as well as feedback strategies, (5) reliable assessment of the patient’s rehabilitation progress, and (6) reduction of the physical effort of the therapists [76–79]. More specifically, the SWs are equipped with multiple systems and modules that allow: • Estimation of biomechanical parameters: Employing wearable sensors (e.g. Inertial Measurement Units (IMUs), Electromyography sensors (EMG)) or sensors mounted on the device (e.g. ultrasonic boards, laser rangefinders, cameras), the SWs can estimate and acquire gait-related information [71]. Specifically, these sensors allow the estimation of gait spatio-temporal parameters such as cadence, speed, step length, stride length, gait symmetry, among others [80]. The estimation of these parameters may be useful to track and quantify the rehabilitation progress of the user. • Intention of movement estimation: To properly adjust the behavior and movement of the SW, several sensors are usually mounted on the forearm supports and handlebars to estimate the patient’s intention of movement [5]. The most common approaches exploit the the resulting force and torque exerted on the walker by the user to generate linear and angular reference velocities [71, 81]. Specifically, admittance-based controllers are often implemented to model the walker as a dynamic system and generate proper movement commands. Other approaches are based on cognitive interaction, where the user is able to control the SW without physically interacting with the device [72, 75]. • Guidance and navigation: Navigation during walker-assisted gait is mainly focused on safety provision while guiding the user through different environments [71]. To this end, obstacle detection and avoidance techniques, as well as map building modules, autonomous localization algorithms, and path following strategies are often required [71]. These functionalities can be implemented whether independently or in conjunction with user interaction modules, in such a way that shared control strategies can be designed to regulate the user’s role during therapy. • Low-level safety provision: Considering that SW must efficiently react to dynamic conditions in the environment, simple safety modules are implemented. These modules commonly use rule-based algorithms, where different conditions and

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constrains are established to limit or stop the movement of the SW [71]. For instance, these modules allow the device to be stopped in presence of stairs or hazardous environments (e.g., glass walls and walkways). Similarly, using distance sensors these modules are able to detect obstacles in front of the device and limit speed depending on proximity to the obstacle [71, 82]. • Feedback strategies: In order to provide cognitive assistance during walking, robotic walkers usually use different feedback strategies to communicate relevant information to the user. For example, during navigation tasks in blind patients, walkers use haptic and auditory strategies to guide the user [81, 83]. Other implementations are based on visual strategies that allow the user to know their performance and status during therapy [5, 84]. • Remote control: Gait rehabilitation therapies often demand close accompanying of physiotherapists to provide postural corrections and therapy monitoring [85]. In this sense, several SWs have implemented remote control strategies, in such a way that the physiotherapist is able to remotely assess the session data, as well as override or control the SW behavior, if required [71, 86, 87]. The particular selection and implementation of the above described features is often referred to as Human–Robot Interaction (HRI) interfaces [88]. These interfaces are particularly aimed at estimating the interaction with the user, and properly adapting the SW behavior. To this end, multiple sensory inputs such as potentiometers, joysticks, force sensors, voice recognition modules and scanning sensors have benn implemented [84, 89]. Moreover, to provide effective environment sensing and adaption while maintaining safety requirements, Robot–Environment interaction (HREi) interfaces are also required [71]. These interfaces are mainly aimed at detecting environmental constrains, such as obstacles and previously defined trajectories [90]. These kind of interfaces are also useful for people with cognitive impairments, as well as visual limitations, as they allow guidance and navigation capabilities. Bearing in mind that these systems may require greater processing capacities, some studies have analyzed the possibility of implementing cloud-based systems [90]. These approaches not only allow the implementation of navigation algorithms, but also allow the implementation of more complex algorithms based on machine learning techniques and artificial intelligence concepts, and thus a better estimation of the interaction with the user and a more robust and efficient rehabilitation process can be achieved. Finally, considering that SWs might interact in complex and dynamic environments, and should simultaneously provide natural, intuitive and safe interaction with the user, Human-Robot-Environment Interaction (HREI) interfaces are usually required [71]. These interfaces are often aimed at providing shared control strategies, which encourage the active participation of the user while the device has also control of the therapy [71]. These strategies can be designed in such a way that the therapist also has an active participation in the therapy, introducing corrective actions by means of remote devices such as joysticks [89].

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5.7 Applications of the AGoRA Project in Therapy and Daily Life Assistance Considering that the AGoRA Project is aimed at providing mobility rehabilitation in both clinical and everyday scenarios, several control strategies were designed to provide assistance in activities of daily living. As presented in previous sections, several devices were developed. On the one hand, the soft actuated exoskeleton TFLEX was designed to assist the ankle joint (See Fig. 5.1). On the other hand, the AGoRA Exoskeleton and the AGoRA Walker were developed to assist patients with higher rehabilitation requirements (See Fig. 5.2).

5.7.1 Sit to Stand Activity with Lower Limb Exoskeleton and Smart Walker In consideration of the advantages including lower limb exoskeletons in assistance scenarios, the AGoRA system was assessed during the execution of ADL, specifically the sit-to-stand activity. For this purpose, the lower limb exoskeleton and the smart walker were employed as an assistance mechanism while the movement is carried out. In this activity, the exoskeleton has the function of assisting the movement as long as the smart walker provides support. The experimental setup was carried out with a healthy subject without restriction in range of motion (ROM) or some disease that affects the motor functions. The exoskeleton was implemented unilaterally to provide assistance in the right lower limb. Additionally, it was configured to assist a 100% level of assistance. In other words, the exoskeleton was in total control of the movement. On the other hand, the participant was equipped with an EMG sensor (Shimmer, Ireland). It was implemented to measure the muscular activity on the assisted limb while the user was developing this activity The results (see Fig. 5.9) show that the use of the lower limb exoskeleton along with the robotic walker generates a decrease in muscle activity in the rectus-femoral and femoral biceps, which are the main muscles that interact in the execution of the sit-to-stand activity. In conclusion, the implementation of assistive devices such as exoskeletons and smart walkers can be beneficial to assist ADL. Future works will be aimed to integrate the exoskeleton in both limbs in order to ensure complete assistance in the user.

5.7.2 Walking a Ramp Using a Smart Walker Bearing in mind that SWs should improve the quality of life and independence of people with motor problems both in clinical and everyday scenarios, it is relevant

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to analyse the assistance capacity of a SW during ADL. In this sense, to exemplify the physical and kinematic interaction between a patient and a SW, a ramp walk task during walker-assisted gait is proposed. As previously mentioned in Sect. 5.6, admittance-based controllers are widely used to generate linear and angular velocities from the physical interaction between the user and the SW. Specifically, this approach is aimed at giving the user the sensation of dynamic physical interaction during gait assistance by modelling the SW as a mass–damper system [71, 81]. Therefore, the controller described by Eqs. 5.3 and 5.4, was used to generate reference velocities from the impulse force and the resulting torque: L(s) =

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configured as assistive, where the dynamics of the device were almost cancelled and the user was able to interact with the device easily. m = 0.5 kg, bl = 4 N.s/m, J = 2.1 kg.m2 /rad, ba = 2 N.m.s/rad

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For the purpose of the ramp task, an older adult was asked to walk up and down a ramp with the AGoRA Walker (See Fig. 5.2) [71]. The ramp was formed by a ushaped path, where each path measured 15 meters with a 10-degree slope. The user had to walk 3 times on the ramp and average measurements were estimated. As an illustration of the results of the ramp tests, Fig. 5.10 shows several kinematic and interaction parameters acquired by the AGoRA Walker. Specifically, Fig. 5.10.a shows the performed trajectories during the tests. Moreover, Fig. 5.10.b describes the linear and angular speeds generated during the ramp up trial. Finally, Fig. 5.10.c illustrates the behaviour of the force and torque signals during the ramp up trial. An important finding during ramp tests is primarily related to the behavior of the walker on sloping paths. Specifically, the admittance controller was observed to respond properly and in a similar manner to tests performed on flat surfaces (See [71]), and thus, it helped the subject to overcome the ramps. This may be a promising feature, as this result suggest a good capability of the device to interact in complex and everyday scenarios without modifying the control strategy.

5.7.3 Stationary Therapy for Treatment of Spasticity with the Ankle Exoskeleton T-FLEX Taking into account the benefits of using robotic devices in rehabilitation, T-FLEX is foraying into the support of physical therapy processes. For this purpose, a preliminary validation was carried out in the treatment of spasticity context. This section presents the obtained results in the mid-term session of a rehabilitation program, which has a total of 18 sessions. In this sense, a pathological subject with 2 in Ashworth scale and limited motion of the ankle joint performed repetitive exercises with T-FLEX in two posture conditions: (1) knee joint flexed at 90◦ and (2) knee joint in the zero position (maximum extension). The postures are focused on the stimulation of different muscle groups, which are involved in the dorsi-plantarflexion movements. T-Flex was configured to achieve the range of motion tolerable by the subject. Additionally, each posture condition had a duration of 20 min for a total of 370 repetitions, with a rest time of 10 min between each posture. The speed was set in 50% of the device capacity, and the frequency between each repetition was 0.6 Hz. Additionally, the subject was equipped with an EMG sensor (Shimmer, Ireland) to measure the electrical activity on the user’s shank, specifically the tibialis anterior and gastrocnemius muscle.

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(a) Performed paths.

(b) Linear and angular speed signals during the ramp up trial.

(c) Force and torque signals on the handlebars during the ramp up trial. Fig. 5.10 Illustration of average kinematic and interaction data for one subject during ramp tests

The results illustrated in Fig. 5.11 show an increase in muscular activity close to 260 and 100% for the gastrocnemius and tibialis anterior muscles, respectively. This value is achieved in the final stage of the first posture condition. In the second posture condition, the tibialis anterior muscle did not register significant changes. However, the gastrocnemius muscle presented an increase of 30% concerning the initial repetitions. These preliminary results suggest that the use of a ankle exoskeleton (T-FLEX) in stationary therapy induce electrical activity in patients with ankle dysfunctions.

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Additionally, repetitive movements could reduce the level of spasticity, and thus enable them to regain movement. Finally, it is required further in-depth analysis for all sessions and with a greater amount of patients to obtain strong conclusions.

5.8 Conclusions Novel platforms for robot-assisted rehabilitation and assistance focused on physical human-robot interaction present interesting solutions for patients and clinical staff, introducing benefits such as the possibility of the patient working semi-autonomously during gait training in real scenarios, and the patient and therapist receiving quantitative measurements of performance improvements from sensor data coming from active exoskeletons and smart walkers. In the context of the AGORA Project, several concepts for developing optimal human-robot interaction in both, rehabilitation and assistance scenarios were reviewed. On the one hand, biomechatronic design focused on development of kinematic compatibility was studied (i.e., targeted activity, human features, mechanical structure and actuators, physical interfaces and physical interaction). Moreover, it can be stated that by offering kinematic compatibility between a lower limb exoskeleton and the subject, the performance and functionality can be improved. Specifically, kinematic compatibility ensures that the torque supplied by the device’s joints is properly transmitted to the subject’s joints. On the other hand, several sensor interfaces to provide robust control parameters to the control strategies and to estimate the patient’ status performing the targeted activities were presented (i.e., gait phases detection and sensors for physical interaction). Specifically, it is possible to detect online the gait phases to properly adapt the control strategies to the human gait, and calculating the torque generated by the subject towards the joint of the exoskeleton by means of strain gauges to enable force control. Additionally, this book chapter presented different control strategies for exoskeletons (variable-impedance controllers for hip—knee robotic exoskeletons, actuation and control systems for ankle exoskeletons), as well as strategies for calculating human-robot interaction forces. Thanks to this advances, it is possible to develop backdrivable devices that allow the subject to use the device and move each robotic joint unrestricted at the time that the subject requires. These features allow the device to be denominated as transparent with respect to the subject. In addition to the above, the instrumentation of a lower limb exoskeleton with sensors such as strain gauges, allowed the acquisition and measurement of the forces and torques involved in the physical interaction. Subsequently, impedance based controllers constitute the basis of physical interaction providing feedback to the control system for rehabilitation and assistive devices such as smart walkers or lower limb exoskeletons. Conversely, ankle exoskeletons require a deep analysis in terms of control and actuation to properly transmit the assistance to the joint.

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Based on those concepts is possible to develop novel therapies and functionalities for assistance in daily life activities, such as: sit to stand activity with lower limb exoskeleton and smart walker, walking in generic environments using a smart walker and stationary therapy for treatment of spasticity with an ankle exoskeleton. Those applications confirm that robots can guarantee an intensive training since they are able to take over the physical demand required to the therapists in both, conventional physical rehabilitation and daily life activities. The level of challenge can be automatically adapted by the control algorithm following the targeted activity and constantly evaluating the patient’s status. Further research should provide a quantitative assessment of the relative importance of the robotic platforms developed in the context of the AGoRA project in a short- and long-term time span and of the outcomes that might come from their combination. Such evidence will promote the optimal design for novel rehabilitation robots and propose new tools for empowering clinicians at rehabilitation settings.

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Correction to: Technologies for Therapy and Assistance of Lower Limb Disabilities: Sit to Stand and Walking Isela Carrera, Hector A. Moreno, Sergio Sierra, Alexandre Campos, Marcela Munera, and Carlos A. Cifuentes

Correction to: Chapter 4 in: M. Cardona et al., Exoskeleton Robots for Rehabilitation and Healthcare Devices, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-15-4732-4_4 The original version of the chapter was published with incorrect authors list. This has now been corrected.

The updated version of this chapter can be found at https://doi.org/10.1007/978-981-15-4732-4_4 © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020 M. Cardona et al., Exoskeleton Robots for Rehabilitation and Healthcare Devices, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-981-15-4732-4_6

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