Control Systems Design of Bio-Robotics and Bio-Mechatronics with Advanced Applications

Control Systems Design of Bio-Robotics and Bio-Mechatronics with Advanced Applications delivers essential and advanced bioengineering information on the application of control and robotics technologies in the life sciences. Judging by what we have witnessed so far, this exciting field of control systems and robotics in bioengineering is likely to produce revolutionary breakthroughs over the next decade. While this book is intended for senior undergraduate or graduate students in both control engineering and biomedical engineering programs, it will also appeal to medical researchers and practitioners who want to enhance their quantitative understanding of physiological processes.

• Focuses on the engineering and scientific principles underlying the extraordinary performance of biomedical robotics and bio-mechatronics
• Demonstrates the application of principles for designing corresponding algorithms
• Presents the latest innovative approaches to medical diagnostics and procedures, as well as clinical rehabilitation from the point-of-view of dynamic modeling, system analysis and control

• Language: English
• Publisher: Academic Press
• Release date: Nov 30, 2019
• ISBN: 9780128174647

Book preview

2013;2:4–11.

Preface

Ahmad Taher Azar Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia, Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt; http://www.bu.edu.eg/staff/ahmadazar14, https://sites.google.com/site/drahmadtaherazar/

Biomedical robotics and biomechatronics research operations can be traced back to the 1970s and 1980s. Biomedical robotics and biomechatronics research cover a variety of fast-growing interdisciplinary areas, including bioinspired robots for industrial, military, medical, and rehabilitation applications. Biomedical robotics and biomechatronic devices are becoming a hot spot for research into technology and science. They have a methodological background in the fields of biomedical engineering and robotics, and are now applied to different engineers, basic and applied science, such as biology, neuroscience, medicine and humanities, as well as sociology, ethics, and philosophy. Knowledge acquisition of the biological system operating mechanism is a major objective of the study of biomedical robot systems and biomechatronic systems. As a consequence, the biological systems are frequently analyzed from a biomechatronic perspective. Knowledge is used to create technological and technological advancements that can lead to the development and construction, through the imitation of insects, pets, humans, and multiple lifetimes, of bioinspired devices and systems.

In future generations of biomedical systems and apps, the combination of robotic technology and in-depth biomedical sciences is also promising. A strategy based on biomedical robotics and biomechatronics is of excellent concern, with three key objectives: (1) enhance knowledge of underpinnings of sensing and acting in diverse animals, including our human beings; (2) build valid and helpful high-efficiency mechatronic and robotic systems and (3) develop efficient biological interactive systems, for example. Research into this direction clearly shows growing interest in humanoid technology; bioinspired and biomimetic robotics; human-robot cooperation and interaction; and biomechatronic endoscopy, intervention, aid, and recovery instruments. In addition to the growing importance of biological inspirational design in the advances in artificial structures, many applications in mechatronics and robotics in different areas present fresh difficulties, both in theory and in technology. Therefore, the technologies and models used in design and manufacture of biomechatronic equipment and biologically inspired robots are very important for further development.

We strive in this book (i) to highlight biomedical robotics and biomechatronical theoretical and practical problems; (ii) to bring together alternatives under distinct circumstances with particular attention to validation of these instruments in biorobotic environments using practical tests; and (iii) to launch important case studies.

About the book

The new Elsevier book, Control Systems Design of Biorobotics and Biomechatronic with advanced applications, consists of 13 contributed chapters by subject experts who are specialized in the various topics addressed in this book. The special chapters have been brought out in this book after a rigorous review process in the broad areas of biorobotics and biomechatronics. Special importance was given to chapters offering practical solutions and novel methods for the recent research problems in the mathematical modeling and control applications of robotic systems. This book aims at showcasing the most exciting and recent advances in the application of biorobotics and biomechatronics in various fields and brings together a broad spectrum of topics covering various definitions, developments, control, and deployment of biomechatronics/robot systems.

Objectives of the book

Through this book, we wish to deliver essential and advanced bioengineering information in applications of control and robotics technologies in life science. In the next few years, there will surely be much more exciting developments in this area. The first objective of this book is to focus on the engineering and scientific principles underlying the extraordinary performance of biomedical robotics and biomechatronics. The second objective is the application of the principles to design the corresponding algorithms that purposively operate in dynamic scenarios.

Organization of the book

This well-structured book consists of 13 full chapters.

Book features

•The book chapters deal with the recent research problems in the areas of biomedical robotics and biomechatronics.

•The book chapters present various applications for biomedical robotic systems.

•The book chapters contain a good literature survey with a long list of references.

•The book chapters are well written with a good exposition of the research problem, methodology, block diagrams, and mathematical techniques.

•The book chapters are lucidly illustrated with simulations.

•The book chapters discuss details of engineering applications and future research areas.

Audience

The book is primarily meant for researchers from academia and industry, who are working in the research areas—control engineering, biomedical engineering, electrical engineering, and computer Engineering. The book can also be used at the graduate or advanced undergraduate level as a textbook or major reference for courses such as Biorobotics, Biomechatronics, Selected topics in biomedical engineering, and many others.

Acknowledgments

As editor, I hope that the chapters in this well-structured book will stimulate further research in Biorobotics and Biomechatronic applications.

I hope that this book, covering so many different topics, will be very useful for all readers.

I would like to thank all the reviewers for their diligence in reviewing the chapters.

Special thanks go to Elsevier, especially the book Editorial Project Manager Emma Hayes and Production Project Manager Nirmala Arumugam.

No words can express my gratitude to the Acquisitions Editor, Sonnini Ruiz Yura, for her great effort and support during the publication process.

Special acknowledgment to Prince Sultan University and Robotics and Internet-of-Things Lab (RIOTU), Riyadh, Saudi Arabia for giving me the opportunity to finalize this book.

Chapter One

Human-robot interaction for rehabilitation scenarios

Jonathan Casas; Nathalia Cespedes; Marcela Múnera; Carlos A. Cifuentes    Department of Biomedical Engineering, Colombian School of Engineering Julio Garavito, Bogotá, Colombia

Abstract

Biomechatronic systems integrate mechanisms, sensor integration, control strategies, human-robot interaction, and signal processing techniques. This chapter stresses the biomechatronic conception of robotic agents that interact with patients undergoing rehabilitation programs. In this context, the main role of social robotic agents is to act as companions or assistants in a specific task, which is intended improve patients’ performance or increase engagement during the therapy. Social robots are required to contain a series of features that allow them to interact in an effective way, providing adaptability and flexibility to human environments. In this work, two robot-based therapy models are presented. The first intervention with a social robot was held in a cardiac rehabilitation service. Subsequently, this work has been extended to a neurological rehabilitation scenario. That way, the corresponding clinical context in which the system was deployed is described, followed by the proposed custom human-computer interface that was designed with two purposes. In the first instance, the interface allows the patient to observe the acquired sensory data during the therapy. Likewise, this system serves as a means of communication between the robot and the user. Two experimental studies were performed in the cardiac and neurological rehabilitation contexts. Important results showed that the robot assistance during the therapy has a positive impact, which is reflected in the decreasing of risk factors such as high heart rate values or fatigue perception and the improvement of patients’ performance indicators such as cervical and thoracic postures. A positive attitude toward the robot role and patients was observed, and also greater motivation for patients to follow the rehabilitation treatment and improve their own performance according to the robot's interaction.

Keywords

Human-robot interaction; Multimodal interfaces; Sensor integration; Social robots; Cardiac rehabilitation and neurological rehabilitation

1 Introduction

Robots are being introduced in an increasing variety of domains. In such areas they are used as a tool for social assistance to help people in their homes, to be a guide in public spaces, as a teacher in classrooms, or as a coach in rehabilitative settings. In this direction, researchers worldwide are studying the social factors related to the human-robot interaction (HRI) in human environments and great attention is being focused on the cognitive human-robot interaction (cHRI) (Santis, 2007). In this chapter, we focus on robots as platforms, companions, and coaches for helping people to exercise and increase their physical abilities after suffering diseases regarding the cardiovascular and/or the neurological systems.

As a brief introduction, the cardiovascular system allows the transport and delivery of nutrients, oxygen, hormones, and blood cells. In addition, it is a self-sealing circuit, that brings tools for repair and healing in case of damage. On the other hand, the nervous system coordinates actions by transmitting and receiving signals from several parts of the body. It consists of two main parts: the central nervous system (CNS) and the peripheral nervous system (PNS). The brain and spinal cord are part of the CNS and the PNS consists mainly of nerves, which connect the CNS to every part of the body. Those systems are susceptible to be affected by several diseases and disorders (see Fig. 1). Some of them will be introduced as follows.

Fig. 1 Cardiovascular and neurological systems of the human body. Common cardiovascular and neurological disorders are illustrated.

Cardiovascular diseases (CVDs) are known as disorders of the heart and blood vessels that include coronary heart disease, cerebrovascular disease, rheumatic heart disease, and other conditions (World Health Organization, 2018a). These diseases are referred to conditions that involve narrowed or blocked vessels and can lead to heart attack, stroke, and heart failure (American Heart Association, 2017). Two groups of CVDs are considered: (1) CVDs caused due to atherosclerosis, such as ischemic heart disease or coronary heart disease (heart attack), cerebrovascular disease (stroke), and diseases of the aorta and arteries that include hypertension and peripheral vascular disease; and (2) CVDs caused due to a different condition, including congenital or rheumatic heart disease, as well as cardiomyopathies and cardiac arrhythmias (World Health Organization, 2015). Among these groups, 70% of CVDs are caused by atherosclerosis (World Health Organization, 2015, 2018a). CVDs take the lives of 17.9 million people every year, an estimated 31% of all deaths worldwide (World Health Organization, 2018b). This situation affects quality of life, as demonstrated by 20% of patients who suffered a CVD event presenting prevalence of depression (Taylor et al., 2006).

Moreover, neurological disorders are diseases of the central and peripheral nervous systems, including the brain, spinal cord, nerves, neurological joints, and muscles, among others (WHO et al., 2006). Neurological disorders comprise an extensive group of heterogeneous (more than 600) pathologies, for example, spinal cord injuries, dementia, stroke, brain tumors, and multiple sclerosis (World Health Organization, 2016). As consequences of neurological disorders, upper and lower limbs can be affected, causing limitations within gait patterns and self-performance of the patients in social, economical, and physiological contexts (WHO et al., 2006). According to the World Health Organization, neurological disorders contributed to 92 million disabilities in 2005, and it is projected that this will increase to 103 million in 2030 (approximately 12%) (WHO et al., 2006). Within this group of diseases, stroke causes more than 6 million deaths each year (World Health Organization, 2016).

Stroke survivors typically show significantly reduced gait speed, shortened step length, and loss of balance in their gait patterns, and often experience falls (Potter et al., 1995). With the proven fact that repetitive and persistent stimulation could restore and reorganize defective motor functions caused by neurological disorders, there is a strong need for new therapeutic interventions (Johansson, 2011).

This chapter aims to present the latest advancements in the area of social robots for rehabilitation scenarios that involve pathologies with high prevalence worldwide, such as CVDs and neurological disorders. The main outlines showed that the use of a social robot in rehabilitation scenarios has a positive impact reflected in the improvement of physiological parameters (e.g., heart rate [HR] and spinal posture patterns) and patients’ motivation to follow the treatment procedures. On the other hand, due to the monitoring provided by the cHRI and the robot interaction, therapists were more focused on other rehabilitation tasks.

This chapter is organized in five thematic sections, addressing relevant aspects regarding social robots for rehabilitation scenarios and the important interaction aspects involved in this process.

Section 2 addresses the literature review concerning social robotic agents (SARs), paying special attention to the interfaces that have been implemented or can be useful for applications in rehabilitation and health care.

Section 3 begins with the definition of human-robot interfaces and the proposed robot-based therapy model. Afterward, the current state of rehabilitation is described. Finally, cHRIs are presented in the context of cardiac and neurological rehabilitation (NR).

Section 4 addresses two experimental studies presented in this chapter: a cardiac rehabilitation (CR) longitudinal study and a study for NR based on repeated measurements.

Section 5 discusses the model developed for social HRI in the context of rehabilitation scenarios and analyzes outcomes obtained during the robot-therapy interventions.

Finally, Section 6 presents the conclusions and some recommendations for future works in this challenging field of rehabilitation robotics.

2 Related work

Socially assistive robotics (SAR) is the field dedicated to the development of robots that provide feedback, instructions, encouragement, and emotional support through social interaction to increase patients’ motivation and performance within the therapies. SAR has been initially defined as the combination of assistive robotics (AR) and social interactive robotics (SIR). In the first place, SAR and AR are meant to provide assistance to human users. However, in SAR, this assistance is specifically achieved by means of social interaction with the user. From this perspective, SAR and SIR have the same goal, as they are focused on developing social interaction strategies that enable them to exhibit a closer and more effective interaction with the human user. Unlike SIR, the scope of SAR is limited to achieve major progress in the areas such as rehabilitation, health care, etc. (Feil-Seifer and Mataric, 2005).

2.1 Social robotic agents

The main role of SARs, or social robots, is to act as companions or assistants in a specific task. In rehabilitation and healthcare environments, social robots are regarded as training assistants, coaches, or motivator agents that help improve patients’ performance or increase engagement during the therapy. With this in mind, social robots are required to contain a series of features that allow them to interact in an effective way, providing adaptability and flexibility to human environments. As these agents are designed to interact socially with humans, they must exhibit human-like behaviors and their appearance and functionality must be structured in a way that humans can interpret and be familiarized with Fong et al. (2003).

In this context, a considerable aspect that enables an effective social interaction is the physical embodiment, which allows the robot to perceive and experience the physical world. Hence, it will be able to interact with humans and engage with their activities in a more natural and intuitive way (Wainer et al., 2006b). The embodiment is a term considered to refer to the fact that intelligence cannot be limited to exist in the form of an abstract algorithm, but requires a physical instantiation or body (Pfeifer and Scheier, 1999; Dautenhahn et al., 2002). Different studies have demonstrated the effect and benefits that embodiment attributes to the robotic platforms over other types of social agents, such as virtual agents and screen-based avatars. It has been demonstrated that social robots receive more attention and the interaction with the users can be more engaging (Belpaeme et al., 2013; Powers et al., 2007; Wainer et al., 2006a).

Although all social robots are embodied (have a physical body that allow them to interact with the world), the degree of interaction may vary depending on the capabilities of the robot. Hence, a robot with more motor and sensor skills will present more capabilities to interact with the environment as it can establish more relationships with the world. Currently, there is a wide spectrum in the design features that social robots have. In this chapter, we consider the classification of social robots in two main categories: (1) Real/Abstract, which indicates the degree of similarity that the platform has with the nature (i.e., how similar the robot is to a living being), unlike the abstract design; and (2) Animal/Human, which describes their similarity to a human being or an animal creature. Fig. 2 illustrates some robots that are conventionally used. As can be observed, these platforms vary in their shape and appearance.

Fig. 2 Socially assistive robots classification. In this chapter we consider two main categories: Real/Abstract , referring to their similarity to living beings, and Human/Animal , referring to their similarity to humans or animals.

Although all these platforms can be regarded as SARs, their functionalities and field of applications can diverge, as each robot can be suitable for a specific task and a specific degree of interaction.

As every day more robotic platforms are designed, the application spectrum of SAR is expanding in a similar way, covering multiple areas in healthcare and rehabilitation scenarios. The next section presents a detailed overview of the applications and the relevant findings associated with this research.

2.2 Applications in rehabilitation and healthcare

SAR was initially explored in cardiovascular therapies with the development of CLARA, a hands-off physical therapy assistant whose aim was to reduce the effects of nursing shortages, provide motivation, and aid patients through the rehabilitation exercises as spirometry therapies. In this study, the researchers found high expectations over the robot's usefulness and an average overall satisfaction of the population of about 80% (Kang et al., 2005). Furthermore, SAR has been used in several applications focusing on elderly care (Bemelmans et al., 2012), dementia, mental health treatments (Rabbitt et al., 2015; Martín et al., 2013), and physical and poststroke rehabilitation (Matarić et al., 2007). Within elderly care areas, robots such as PARO were used in therapeutic scenarios, in order to achieve social exchanges and encourage patients during exercises (Marti et al., 2006). The study opens interesting perspectives about the use of a robot as a nonpharmacological therapeutic aid. It has been found that PARO was able to support the complexity of a clinical scenario in a flexible way allowing patients’ engagement and sociorelational exchanges. In addition, effects such as the improvement of communication, cognitive skills (Tsardoulias et al., 2017), and reduction of anxiety (Louie et al., 2014) in elderly populations have been observed, showing in general positive attitudes toward social robots.

Another domain that has been broadly approached by SAR technology is poststroke rehabilitation. Autonomous robots (Matarić et al., 2007; Mead et al., 2010) and embodied agents (Jung et al., 2011) have been explored to monitor and supervise poststroke survivors during gait training and upper-limb exercises. The studies showed a positive impact within the users on their willingness to perform prescribed rehabilitation, changes in the motor functioning, and improvements in the average number of trials accomplished per minute.

Finally, physical training and coaching are areas of interest in social robotics as robots can be used as companions to guide different kind of exercises and improve the adherence to this programs using cognitive approaches. As an example, NAO robots were implemented into conventional physiotherapy practices in order to guide several body movements (López Recio et al., 2013) and in upper-limb exercises for patients with physical impairments, such as cerebral palsy and obstetric brachial plexus palsy (Pulido et al., 2016). The results have demonstrated an accurate monitoring of the therapies, and fluent interaction with the robot. In addition, patients like to follow the exercises provided by the NAO and engage with the rehabilitation trying to perform the tasks (Pulido et al., 2016). In 2008, a long-term study showed the effects of HRI in coaching with the aim of reducing the rates of overweight and obesity. In this case, the robot asked patients their diet goals in terms of burning calories during exercise and data related to the food consumed during the day. The results showed that the participants assisted by the social robot were more interested in knowing their calorie consumption and exercise performed than those who used other methods (Kidd and Breazeal, 2008).

Adherence is an important factor to achieve exercise adoption, and different studies have shown positive results regarding this factor. Gadde et al. (2011) evaluated in the early stages an interactive personal robot trainer (RoboPhilo) to monitor and increase exercise adherence in older adults. The system was proved with 10 participants, showing initially a positive response and a favorable interaction. A complementary application where robots are being used to motivate and increase adherence in long-term therapies and medical self-care is diabetes mellitus treatments, where robots play the role of personal assistants to adults (Looije et al., 2006) and children (Baroni et al., 2014). This has shown potential results within motivational aspects and treatment engagement.

Summarizing, several authors have described SAR systems in terms of aiding patients in different areas showing great potential and results. This research focuses on deploying a social robot into cardiovascular and NR scenarios to provide monitoring and motivation during therapeutic treatment.

3 Human-robot interfaces for rehabilitation scenarios

Human beings interact with the environment through cognitive processes, sequences of tasks that include reasoning, planning, and finally the execution of a previously identified problem or goal. From this process, the robots may use information regarding human expressions and/or physiological phenomena to adapt, learn, and optimize their functions, or even to transmit back a response resulting from a cognitive process performed within the robot. This concept is named the cHRI (Pons et al., 2008).

cHRI systems often present bidirectional communication channels. On the one hand, robot's sensors measure the physiological parameters, human actions, and expressions. On the other hand, the actuators transmit the robot's cognitive information (social interaction) to the user. In other words, the user observes the state of the system through feedback sent immediately after the user command is executed. This configuration performs a closed-loop HRI in order to develop a natural cooperation during the rehabilitation task.

Humans perceive the environment in which they live through their senses: vision, hearing, touch, smell, and taste. They act on the environment using their actuators, for example, muscles, to control body segments, hands, face, and voice. Human-to-human interaction is based on sensory perception of actuator actions. A natural communication among humans also involves multiple and concurrent modes of communication (Sharma et al., 1998).

The goal of effective interaction between a user and their robot assistant makes it essential to provide a number of broadly utilizable and potentially redundant communication channels. This way, any HRI system that aspires to have the same naturalness should be multimodal. Different sensors can, in that case, be related to different communication modalities (Sharma et al., 1998). The integration of classic human-computer interfaces (HCi) like graphical input-output devices, with newer types of interfaces, such as speech or visual interfaces, tactile sensors, laser range finder (LRF) sensors, inertial measurement units (IMU), and physiological sensors, facilitates this task.

3.1 Proposed robot-based therapy model

The robot-therapy model proposed in this chapter is illustrated in Fig. 3. This schema considers two main components: (1) Motivation, which aims to provide intrinsic and extrinsic motivation, and (2) Therapy control, focused on the monitoring of the therapy performance, the management of warning events, and reduction of risk factors that can lead to emergencies. Each therapy has risk factors associated with the tasks that are performed. The model seeks to manage and reduce these risk factors while monitoring the development of physiological and spatiotemporal parameters that are relevant to each scenario. This goal is achieved by means of custom multimodal sensor interfaces that provide all relevant information to be processed by the social robot. Hence, the robot is able to generate feedback to the user appropriate to the context and the therapy conditions.

Fig. 3 Proposed robot-based therapy model. This model considers two main components: motivation and therapy control as the main features that the social robotic agent exhibits during the therapy.

The remainder of this section presents in detail both scenarios, cardiac and NR, where a social robotic platform was deployed in the framework of the model previously described.

3.2 Rehabilitation scenarios

Considering the robot-based therapy model described earlier, two interventions were carried out. The first intervention with a social robot was held in the CR service. Subsequently, this work has been extended to the NR scenario. This section describes the corresponding clinical context, in which the system was deployed, followed by the proposed custom cHRI designed for each application.

3.2.1 Current state of cardiac rehabilitation

CR programs are designed to prevent CVDs or to treat a patient after a cardiovascular event. CR covers different areas, such as nutrition, physical exercise, and health education. Even though these programs have proved successful in reducing and preventing the occurrence of a posterior cardiovascular event, the adherence associated with the program does not reach a desirable level. Multiple studies have demonstrated the low adherence rates, which are not higher than 50%, and the implications that this situation has in the health condition of patients (Bethell et al., 2011; Sarrafzadegan et al., 2007; Worcester et al., 2004).

Different studies have evaluated the adherence in rehabilitation programs. From these studies, it has been found that patients are more likely to attend the programs when physiotherapists encourage them during the sessions and feel satisfied with the therapy. Likewise, when there is a perceived interest by the therapist and patients feel a sense of complete supervision, their performance and results tend to increase (Essery et al., 2017; Jackson et al., 2005). In this context, it is clear what role the continuous monitoring and encouragement of the medical staff plays in the success, in terms of adherence and performance, of patients attending to the rehabilitation therapies. Therefore, the work presented in this section focuses on the development of an assistive tool, based on SAR, that supports the work carried out by clinicians and aims to offer a more personalized service to the patients, through continuous monitoring, motivation, and companionship within the CR therapies.

Before introducing the proposed SAR system, it is worth mentioning the structure that a conventional CR program has. The structure and components differ depending on the country and institution. However, they traditionally consist of three phases: inpatient (phase I), outpatient (phase II), and community maintenance (phases III and IV). The outpatient phases, namely phases II and III, take place in a specialized center or institution and are carefully performed under the supervision of healthcare providers with monitoring based on exercise tolerance test results (Kim et al., 2011). Our work has been focused primarily on phase II of the CR programs. The features and structure of this phase are described in following sections.

Phase II

This phase is the first outpatient phase and begins immediately after the patient leaves the hospital. It consists of a combination of physical exercise on a treadmill and an education program oriented to prevention of risk factors, as well as adoption of healthy habits (e.g., controlling blood pressure, cholesterol, weight and stress management). This phase has an average duration of 3 months and is designed to provide a safe monitored environment for exercise. The monitoring consists of measuring the patient's blood pressure, HR, and eventually heart and lungs sounds. Additionally, it is important to monitor the perceived exertion level (i.e., fatigue or effort during the exercise). This measurement is carried out with the Borg Scale (BS), which is a qualitative measurement that estimates the perceived exertion of the patient (6 for low intensity and 20 for very high intensity). As a result from phase II, the patient should be able to self-monitor their physiological parameters and exertion levels. This aspect will return the confidence to the patient to continue a normal life, being aware of their health condition and the healthy lifestyle that is required to prevent a second cardiac event (Scherr et al.,