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Neurologic injuries, such as stroke and spinal cord injuries (SCI), cause damage to neural systems and motor function, which results in lower limb impairment and gait disorders. Subjects with gait disorders require specific training to regain functional mobility. Traditionally, manual physical therapy is used for the gait training of neurologically impaired subjects which has limitations, such as the excessive workload and fatigue of physical therapists. The rehabilitation engineering community is working towards the development of robotic devices and control schemes that can assist during the gait training. The initial prototypes of these robotic gait training orthoses use conventional, industrial actuators that are either extremely heavy or have high endpoint impedance (stiffness). Neurologically impaired subjects often suffer from severe spasms. These stiff actuators may produce forces in response to the undesirable motions, often causing pain or discomfort to patients. The control schemes used by the initial prototypes of robotic gait training orthoses also have a limited ability to provide seamless, adaptive, and customized robotic assistance. This requires new design and control methods to be developed to increase the compliance and adaptability of these automated gait training devices. This research introduces the development of a new robotic gait training orthosis that is intrinsically compliant. Novel, assist-as-needed (AAN) control strategies are proposed to provide adaptive and customized robotic assistance to subjects with different levels of neurologic impairments. The new robotic gait training orthosis has six degrees of freedom (DOFs), which is powered by pneumatic muscle actuators (PMA). The device provides naturalistic gait pattern and safe interaction with subjects during gait training. New robust feedback control schemes are proposed to improve the trajectory tracking performance of PMAs. A dynamic model of the device and a human lower limb musculoskeletal model are established to study the dynamic interaction between the device and subjects. In order to provide adaptive, customized robot assisted gait training and to enhance the subject's voluntary participation in the gait training process, two new control schemes are proposed in this research. The first control scheme is based on the impedance control law. The impedance control law modifies the robotic assistance based on the human subject's active joint torque contributions. The levels of robot compliance can be selected by the physical therapist during the impedance control scheme according to the disability level and stage of rehabilitation of neurologically impaired subjects. The second control scheme is proposed to overcome the shortcomings of impedance control scheme and to provide seamless adaptive, AAN gait training. The adaptive, AAN gait training scheme is based on the estimation of the disability level of neurologically impaired subjects based on the kinematic error and adapts the robotic assistance accordingly. All the control schemes have been evaluated on neurologically intact subjects and the results show that these control schemes can deliver their intended effects. Rigorous clinical trials with neurologically impaired subjects are required to prove the therapeutic efficacy of the proposed robotic orthosis and the adaptive gait training schemes. The concept of intrinsically compliant robotic gait training orthosis, together with the trajectory tracking and impedance control of robotic gait training orthosis are the important contributions of this research. The algorithms and models developed in this research are applicable to the development of other robotic devices for rehabilitation and assistive purposes. The major contribution of the research lies in the development of a seamless, adaptive AAN gait training strategy. The research will help in evolving the field of compliant actuation of rehabilitation robots along with the development of new control schemes for providing seamless, adaptive AAN gait training.
The field of robot-assisted rehabilitation has seen significant development in recent years. With the development of compliant robots that can be safely used in proximity to people, the use of robots to assist rehabilitation has increased rapidly. The need for gait rehabilitation robots arises from the increasing number of people who are affected by conditions that impair their ability to walk. These conditions can include neurological disorders such as strokes, spinal cord injuries, and traumatic brain injuries. In traditional gait rehabilitation, patients receive manual therapy from a team of physical therapists. While manual therapy can be effective, it can also be time-consuming and resource-intensive, and therapists may not be able to provide consistent and precise support to patients. Gait rehabilitation robots, on the other hand, provide a consistent and precise form of therapy that may help patients make faster and more significant progress. Gait rehabilitation robots can also help reduce the physical demands on therapists and improve the efficiency of therapy sessions. This can allow more patients to receive therapy, which can improve access to care and reduce the burden on health care systems. However, most of existing robotic orthoses have not applied appropriate self-aligning mechanism, gravity-balancing mechanism, or compliant actuators. These limitations should be considered in this proposed research. This thesis proposes a novel intrinsically compliant gait rehabilitation robot with multiple actuated degrees-of-freedom (DOFs). The robot design is flexible and can be personalised with the use of telescopic pelvis, thigh, and shank sections. This newly designed rehabilitation robotic orthosis has multiple actuated and passive DOFs. Because of the importance of alignment between the designed rehabilitation robot joints and human anatomical joints, the robot design has a self-aligning mechanism. A novel gear-couple mechanism, toothed cam-couple mechanism and four-bar linkage mechanism are designed and applied to the hip, knee, and ankle joints to align the robot joints with anatomical joints during gait rehabilitation. Simulation-based and motion capture system-based tests are applied to those three mechanisms to evaluate and choose the most effective self-aligning mechanism. The gear-couple mechanism is finally chosen to be applied to the prototype design. A partial gravity-balancing mechanism is also applied to the designed rehabilitation robot. Gravity-balancing can help overcome the inertia of the rehabilitation robot and can further help reduce joint misalignment. The compliance in the robot is intrinsic due to the use of pneumatic muscle actuators (PMAs). The PMAs have been carefully selected to provide the required torques at the hip, knee and ankle joints during gait rehabilitation. Mechanical amplification of the actuation from the PMAs has been achieved by using gear-couples to replace the usual revolute robot joints. However, with the increase in flexibility of the designed prototype and application of PMAs, which are nonlinear actuators, it is challenging to design the robot control system. This challenge was overcome by developing a system dynamic identification model based on the Koopman operator for the design of a nonlinear model predictive controller (NMPC). The new robot design, together with its self-aligning and gravity-balancing mechanisms, is discussed in detail in this thesis. Compliant actuation and its amplification are explained and various algorithms that are designed and implemented on the robot system as robot firmware are explained. A NMPC is designed and developed to control the rehabilitation robot. The experimental setup and evaluation of the robot design, together with the nonlinear model predictive controller, was carried out with healthy users and yielded the intended results. The robotic orthosis along with the NMPC could successfully guide the healthy human subject along the pre-defined trajectory.
Repetitive and task-oriented movements can strengthen muscles and improve walking capabilities among patients experiencing gait impairments due to neurological disorders. The demand for effective rehabilitation is high, given the large number of patients suffering from gait impairments. The traditional physiotherapy is laborious, may not provide the desired cadence and gait patterns, and requires constant presence of physiotherapists. This often leads to delayed treatment for many patients due to the high demand and a shortage of physiotherapists. Early phase post-stroke gait rehabilitation is crucial, as the ability to recuperate lost muscular abilities reduces over time. Lower limb wearable rehabilitation robots have shown promise in improving the locomotor capabilities of patients experiencing gait impairments and reducing the burden on physiotherapists. However, the high cost of commercially available robots makes this technology inaccessible to many hospitals and rehabilitation centers. To address this issue, ongoing research is focusing on improving existing rehabilitation robots in terms of ease of use, innovative design, and cost reduction. Closed-loop linkage mechanisms have recently drawn attention in the development of gait rehabilitation robots due to their ability to address the drawbacks of commercially available robot orthoses. These mechanisms are affordable and capable of providing suitable trajectories for gait training therapy. One of the challenging aspects in designing linkage-based robots is determining and calculating linkage parameters that will produce the required gait trajectories. This thesis presents an innovative approach to synthesizing the linkage dimensions to provide natural gait trajectories. Additionally, it introduces a novel and affordable robotic orthosis based on Stephenson III's six-bar linkage. The developed gait rehabilitation orthosis is a bilateral system powered by a single actuator on each side of the leg, capable of providing naturalistic knee and ankle joint motions relative to the hip joint, which are required during therapeutic gait training. This orthosis can be used in clinical settings and is actuated using only a single motor, yet it is capable of providing complex lower limb trajectory motions at its end-effector. The initial design optimization was carried out using a genetic algorithm (GA), and a deep generative neural network model was developed for the linkage synthesis problem. This model represents an advancement in current kinematic synthesis methods, enabling it to generate dimensions of the links that satisfy various required target human lower limb trajectories during walking in a short period. It will assist designers in determining optimal linkage dimensions to generate the required end-effector trajectories within a single mechanism. To enhance the mechanism's velocity regulation control scheme and address fluctuations that may occur during operation due to external disturbances such as fixed patient's leg and inertia in closed loop linkage mechanisms, a Deep Reinforcement Learning control scheme was proposed to regulate the speed of the input crank to reach satisfactory performance needed for gait rehabilitation training. Experimental evaluations with healthy human subjects were conducted to demonstrate that the mechanism is capable of directing lower limbs on naturalistic gait trajectories with a required walking speed. Furthermore, given the varied disability levels among neurologically impaired patients, the orthosis incorporates a patient cooperative control strategy. This is achieved through the application of impedance learning control, operating on an "assist-as-needed" principle. This innovative approach enables the robot to modify the assistive force it provides during gait cycle aligning with the patient's disability level and contributing towards active participation during the gait rehabilitation training. The proposed control scheme was evaluated in two distinct gait training modes while being worn by a human subject. In the "passive" mode subjects refrained from moving their legs, allowing the robot to guide their movements. While during the second 'active' mode, the subject engaged in normal walking activity while wearing the robot. Experimental results with healthy human subjects indicated reduced robot torques consequent to an increase in human torque. These results substantiate that customized robotic assistance based on the individual needs of patients can enhance their participation, which is essential to improve the treatment outcomes. The concept of this research lies in the development of a novel, affordable, and adaptable robotic orthosis based on Stephenson III's six-bar linkage mechanism, capable of delivering naturalistic individualized lower limb motion. It advances the fields of dimensional synthesis of closed loop linkage mechanisms rehabilitation robotics with the use of deep generative neural network and a Deep Reinforcement Learning control scheme for enhanced velocity regulation. Moreover, the application of impedance learning control encourages active patient participation in gait rehabilitation training by customizing assistive force based on the patient's disability level. With these advancements, the research contributes significantly to the development of more cost-effective, adaptable, and efficient robotic gait rehabilitation systems, presenting a promising solution for improving therapeutic outcomes for patients with gait impairments due to neurological disorders.
Gait disorder is a commonly lasting side-effect for stroke and spinal cord injury survivors. Conventional gait rehabilitation trainings provided by therapists are largely dependent on their experience. Such trainings are often challenging for the therapists due to their physically intensive nature. Hence, consistent optimal results cannot always be achieved. Robotic technologies were thus introduced to automate the gait rehabilitation trainings, in order to emancipate therapists from physically intensive work as well as making rehabilitation training more accessible to patients Research have shown that task specific repetitive training and patients' active participation can lead to more effective gait rehabilitation. However, conventional trajectory tracking controlled robotic gait rehabilitation could change the dynamics of the walking task, reduce inputs from patients' motor systems, lower their physical effort and thus result less effective outcomes. Therefore, it is important to ensure that the robotic gait rehabilitation training is more analogous to actual human walking and maximize the training subject's active participation. The goal of this thesis is the development of a new robotic GAit Rehabilitation EXoskeleton (GAREX) that is compliant with the current neurorehabilitation theories in order to achieve optimised robotic gait rehabilitation. Such goal is tackled systematically in terms of both robotic design and control algorithm research. GAREX was designed to provide safe, task specific gait rehabilitation to stroke patients. Pneumatic muscles (PM) actuators were used to drive GAREX, due to their high power/force to weight ratio and intrinsic compliance. Specially, the intrinsic compliance can create a wide range of dynamic environment for control strategy development. However, the negative correlation between PM's force output and contracting length means a trade-off between torque and range of motion specifications of the actuation system. The design of GAREX comprehensively addressed torque and joint range of motion requirements imposed by task-specific gait rehabilitation training. Control strategies are the key to implement the training theories into robotic operations. In order to encourage patients' active participation, the robot should be controlled to supply just enough guidance/assistance a patient needs to complete treadmill based gait training. To implement assist-as-needed (AAN) concept, the robot should also be able to assess the extent of active participation and change the assistance provided accordingly. The intrinsic compliance of GAREX's PM actuation system could be utilized to change the level of guidance. A new multi-input-multi-output (MIMO) sliding model (SM) controller was developed to adjust assistance while guiding training subjects to walk in predefined gait trajectories. Technical experimental validation indicated that controller was able to track reference gait trajectories and the desired joint space average antagonistic PM pressures. A study with 12 healthy subjects revealed strong statistical evidence that the proposed MIMO SM controller is able to vary the compliance of the exoskeleton To online assess the training patient's active participation, a fuzzy logic compliance adaptation (FLCA) controller is proposed. The FLCA algorithm utilizes the robotic kinematics and human- exoskeleton interaction torque of the knee joint, to estimate the extent of the patient's active participation. Based on the estimation, the desired compliance level can be automatically adjusted with higher compliance for more active participation and vice versa. Nevertheless, the FLCA algorithm does not require models of the exoskeleton and biomechanics of the training subject, which means less preparation work and easier implementation. Performance of the FLCA control system was validated with three healthy subjects who simulated different extents of participation. The FLCA control system could successfully adapt the joint actuation compliance accordingly in all the scenarios.
The concepts represented in this textbook are explored for the first time in assistive and rehabilitation robotics, which is the combination of physical, cognitive, and social human-robot interaction to empower gait rehabilitation and assist human mobility. The aim is to consolidate the methodologies, modules, and technologies implemented in lower-limb exoskeletons, smart walkers, and social robots when human gait assistance and rehabilitation are the primary targets. This book presents the combination of emergent technologies in healthcare applications and robotics science, such as soft robotics, force control, novel sensing methods, brain-computer interfaces, serious games, automatic learning, and motion planning. From the clinical perspective, case studies are presented for testing and evaluating how those robots interact with humans, analyzing acceptance, perception, biomechanics factors, and physiological mechanisms of recovery during the robotic assistance or therapy. Interfacing Humans and Robots for Gait Assistance and Rehabilitation will enable undergraduate and graduate students of biomedical engineering, rehabilitation engineering, robotics, and health sciences to understand the clinical needs, technology, and science of human-robot interaction behind robotic devices for rehabilitation, and the evidence and implications related to the implementation of those devices in actual therapy and daily life applications.
The field of mechatronics integrates modern engineering science and technologies with new ways of thinking, enhancing the design of products and manufacturing processes. This synergy enables the creation and evolution of new intelligent human-oriented machines. The Handbook of Research on Advancements in Robotics and Mechatronics presents new findings, practices, technological innovations, and theoretical perspectives on the the latest advancements in the field of mechanical engineering. This book is of great use to engineers and scientists, students, researchers, and practitioners looking to develop autonomous and smart products and systems for meeting today’s challenges.
Rehabilitation Robotics gives an introduction and overview of all areas of rehabilitation robotics, perfect for anyone new to the field. It also summarizes available robot technologies and their application to different pathologies for skilled researchers and clinicians. The editors have been involved in the development and application of robotic devices for neurorehabilitation for more than 15 years. This experience using several commercial devices for robotic rehabilitation has enabled them to develop the know-how and expertise necessary to guide those seeking comprehensive understanding of this topic. Each chapter is written by an expert in the respective field, pulling in perspectives from both engineers and clinicians to present a multi-disciplinary view. The book targets the implementation of efficient robot strategies to facilitate the re-acquisition of motor skills. This technology incorporates the outcomes of behavioral studies on motor learning and its neural correlates into the design, implementation and validation of robot agents that behave as ‘optimal’ trainers, efficiently exploiting the structure and plasticity of the human sensorimotor systems. In this context, human-robot interaction plays a paramount role, at both the physical and cognitive level, toward achieving a symbiotic interaction where the human body and the robot can benefit from each other’s dynamics. Provides a comprehensive review of recent developments in the area of rehabilitation robotics Includes information on both therapeutic and assistive robots Focuses on the state-of-the-art and representative advancements in the design, control, analysis, implementation and validation of rehabilitation robotic systems
The field of rehabilitation has undergone tremendous transformation in recent years. From conventional forms of rehabilitative therapy that included a monotonous, repetitive exercise to the inclusion of rehabilitation robots to make the therapeutical treatment less daunting. Rehabilitation robots provide an objective, engaging, and inexpensive alternative to traditional practices while reducing the burden on the healthcare system as well as the patient. However, in the last few decades, the development of such devices was more focused on the lower limb. Due to the complexity of movements, the devices available for rehabilitating the wrist are limited in the literature. Therefore, this research aims to develop a compliant parallel robot for wrist rehabilitation in three degrees of rotational freedom: Pronation/Supination (PS), Flexion/Extension (FE), and Adduction/Abduction (AA). Novel control strategies have been developed to guide the robot in assisting the patient in achieving the rehabilitative goal. The developed prototype follows an end-effector design with a parallel mechanism. Intrinsically compliant Biomimetic Muscle Actuators (BMA) power the prototype and provide the necessary movement. Since these actuators have inherent hysteresis and transient characteristics, a heuristic model has been developed to provide an accurate and time-efficient relationship. The rehabilitation robots, by definition, work in proximity to the human subject and work in partnership; hence, physical interaction is certain. The physical human-robot interaction has highly nonlinear and uncertain dynamics. Therefore, the Koopman Operator theory has been employed to develop a system identification model. The Koopman Operator is a mathematical tool that linearizes highly nonlinear dynamical systems by lifting the state space into an infinite dimensional space. This data-driven approach helps identify the nonlinear system dynamics and develop a trajectory-tracking controller for wrist rehabilitation. The effectiveness of the Koopman Operator is also tested in designing an adaptive controller to predict anatomical stiffness. In a healthy person, the anatomical stiffness is accommodated by the neuromuscular system, which is affected by stroke. Hence, a successful controller should adapt to the anatomical stiffness and the altered physiological capabilities. The Koopman Operator was used to develop the model for predicting anatomical stiffness, which depends on the axis of rotation and the geometric orientation. Rehabilitation therapy can be considered a joint task undertaken by the human subject and the robot with physical interaction. In other words, it can be deemed a coordination game with the human and the robot as the two players. The human's strategy is unknown to the robot, but the controller should interpret the human subject's intention. Therefore, an adaptive estimation method was developed to estimate the human's intention and then assist them in achieving the goal while fulfilling the common objective of wrist rehabilitation. The concept of modeling the human subject and the robot as two agents with a common goal are then extended to exploring them as two independent energy sources. As active human participation is crucial for prompt recovery, the robot is expected to decrease its energy dissipation to increase the level of involvement from the patient. An autodidactic algorithm was developed to estimate the transactive energy between humans and robots during physical interaction. The energy dissipation of the human and the robot was mapped for each orientation attained during the rehabilitation session. The physiological capabilities and the effects of stroke vary from patient to patient. Therefore, it is crucial that the controller can adapt to diverse needs. Accordingly, smart avatars were programmed to learn from the human subject in real-time and provide an energy-efficient rehabilitation trajectory. The smart avatars included a controller with energy optimization to modify the trajectory to minimize the robot's energy dissipation and an Inverse Dynamics model to simulate the subject and estimate the subject's involvement. The avatar was then appended with an Assist-as-Needed controller that calculates the robot's participation in achieving the goal successfully. The essential contributions of this research are the development of an intrinsically compliant parallel robot for wrist rehabilitation with energy-efficient control algorithms. The algorithms developed in this research were successfully tested with healthy human subjects; however, extensive clinical trials with neurologically impaired subjects are required to establish the efficiency of the proposed prototype.
This book addresses cutting-edge topics in robotics and related technologies for rehabilitation, covering basic concepts and providing the reader with the information they need to solve various practical problems. Intended as a reference guide to the application of robotics in rehabilitation, it covers e.g. musculoskeletal modelling, gait analysis, biomechanics, robotics modelling and simulation, sensors, wearable devices, and the Internet of Medical Things.
Focussing on the key technologies in developing robots for a wide range of medical rehabilitation activities – which will include robotics basics, modelling and control, biomechanics modelling, rehabilitation strategies, robot assistance, clinical setup/implementation as well as neural and muscular interfaces for rehabilitation robot control – this book is split into two parts; a review of the current state of the art, and recent advances in robotics for medical rehabilitation. Both parts will include five sections for the five key areas in rehabilitation robotics: (i) the upper limb; (ii) lower limb for gait rehabilitation (iii) hand, finger and wrist; (iv) ankle for strains and sprains; and (v) the use of EEG and EMG to create interfaces between the neurological and muscular functions of the patients and the rehabilitation robots. Each chapter provides a description of the design of the device, the control system used, and the implementation and testing to show how it fulfils the needs of that specific area of rehabilitation. The book will detail new devices, some of which have never been published before in any journal or conference.