Lower limb disabilities caused by factors such as amputation, neuromuscular impairment, and traumatic injury can significantly impact mobility and quality of life. To restore impaired functionality, individuals may use assistive devices such as prostheses, orthoses, or exoskeletons. However, most of the commercially available lower-limb prosthetic devices are passive and do not provide adequate energy or range of motion compared to natural limbs. This leads to compensatory movements, increased moments on the intact side, and fatigue, especially when performing high-energy tasks like stair a...

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.

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.

Stroke is the leading cause of neurological disability in the US and stroke patients typically require an extensive rehabilitation therapy to regain some of their lost neuromuscular functionality. Various robotic devices have been developed for post-stoke rehabilitation to reduce the labor intensity of therapists and rehabilitation cost and provide therapists with quantitative information about rehabilitation procedure and patients recovery. The robot-patient interaction plays an important role in effectiveness of robotic therapy. Different strategies have therefore been proposed and employed to control rehabilitation robots in order to improve the therapy outcome. However, since the underlying neural mechanisms of motor recovery after stroke are not completely understood, and the effect of each stroke is unique and can be very different from that of other strokes, it is not clear what control strategy is the best. Adaptive assist-as-needed (AAN) control is a movement training methodology with very desirable characteristics for rehabilitation robotic applications. It can adaptively modulate the level of robotic assistance to promote patient active involvement in therapy. In this research, the evolution of two robotic devices for stroke rehabilitation is presented. The FINGER (Finger Individuating Grasp Exercise Robot) rehabilitation robot was designed to assist with hand and finger rehabilitation. A discrete performance-based adaptive control was implementer on FINGER that could provide patients with a suitable assistance level in order to modulate success during therapy game play. In a separate experiment, an inertial and directionally dependent AAN controller was implemented and tested on the FINGER robot. Additionally, the design and development of a 4-DOF parallel robot for upper extremity impairment rehabilitation is presented. This end-effector type rehabilitation robot has low end-effector inertia, is very backdrivable, and is designed to counter-balance a significant portion of its own weight in order to reduce the need for the robot's actuators to overcome gravitational forces. Finally, an inertial adaptive AAN controller is proposed and tested using dynamic simulations.

Stroke rehabilitation technologies have focused on reducing treatment cost while improving effectiveness. Rehabilitation robots are generally developed for home and clinical usage to: 1) deliver repetitive and stimulating practice to post-stroke patients, 2) minimize therapist interventions, and 3) increase the number of patients per therapist, thereby decreasing the associated cost. The control of rehabilitation robots is often limited to black- or gray-box approaches; thus, safety issues regarding the human-robot interaction are not easily considered. Furthermore, despite numerous studies of control strategies for rehabilitation, there are very few rehabilitation robots in which the tasks are implemented using optimal control theory. Optimal controllers using physics-based models have the potential to overcome these issues. This thesis presents advanced impedance- and model-based controllers for an end-effector-based upper extremity stroke rehabilitation robot. The final goal is to implement a biomechanically-plausible real-time nonlinear model predictive control for the studied rehabilitation system. The real-time term indicates that the controller computations finish within the sampling frequency time. This control structure, along with advanced impedance-based controllers, can be applied to any human-environment interactions. This makes them promising tools for different types of assistive devices, exoskeletons, active prostheses and orthoses, and exercise equipment. In this thesis, a high-fidelity biomechatronic model of the human-robot interaction is developed. The rehabilitation robot is a 2 degree-of-freedom parallelogram linkage with joint friction and backlash, and nonlinear dynamics. The mechatronic model of the robot with relatively accurate identified dynamic parameters is used in the human-robot interaction plant. Different musculoskeletal upper extremity, biomechanic, models are used to model human body motions while interacting with the rehabilitation robot model. Human-robot interaction models are recruited for model-in-loop simulations, thereby tuning the developed controllers in a structured resolution. The interaction models are optimized for real-time simulations. Thus, they are also used within the model-based control structures to provide biofeedback during a rehabilitation therapy. In robotic rehabilitation, because of physical interaction of the patient with a mechanical device, safety is a fundamental element in the design of a controller. Thus, impedance-based assistance is commonly used for robotic rehabilitation. One of our objectives is to achieve a reliable and real-time implementable controller. In our definition, a reliable controller is capable of handling variable exercises and admittance interactions. The controller should reduce therapist intervention and improve the quality of the rehabilitation. Hence, we develop advanced impedance-based assistance controllers for the rehabilitation robot. Overall, two types of impedance-based (i.e., hybrid force-impedance and optimal impedance) controllers are developed and tuned using model-in-loop simulations. Their performances are assessed using simulations and/or experiments. Furthermore, their drawbacks are discussed and possible methods for their improvements are proposed. In contrast to black/gray-box controllers, a physics-based model can leverage the inherent dynamics of the system and facilitate implementation of special control techniques, which can optimize a specific performance criterion while meeting stringent system constraints. Thus, we present model-based controllers for the upper extremity rehabilitation robot using our developed musculoskeletal models. Two types of model-based controllers (i.e., nonlinear model predictive control using external 3-dimensional musculoskeletal model or internal 2-dimensional musculoskeletal model) are proposed. Their performances are evaluated in simulations and/or experiments. The biomechanically-plausible nonlinear model predictive control using internal 2-dimensional musculoskeletal model predicts muscular activities of the human subject and provides optimal assistance in real-time experiments, thereby conforming to our final goal for this project.

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.

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.

Research on assistive technologies is undergoing many developments in its effectiveness in helping those with varying impairments. New technologies are constantly being created, researched, and implemented for those who need these technological aides in daily life. Assistive Technologies for Physical and Cognitive Disabilities combines worldwide cases on people with physical and cognitive disabilities with the latest applications in assistive technologies. This reference work brings different researchers together under one title to discuss current findings, developments, and ongoing research in the area of rehabilitative technology. This reference book is of critical use to professionals, researchers, healthcare practitioners, caretakers, academicians, and students.

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