Current health services are struggling to provide optimal rehabilitation therapy to victims of stroke. This has motivated researchers to explore the use of robotic devices to provide rehabilitation therapy for strokepatients. This paper reviews the recent progress of upper limb exoskeleton robots for rehabilitation treatment of patients with neuromuscular disorders. Firstly, a brief introduction to rehabilitation robots will be given along with examples of existing commercial devices. The advancements in upper limb exoskeleton technology and the fundamental challenges in developing these devices are described. Potential areas for future research are discussed.

: Studies of stroke patients undergoing robot-assisted rehabilitation have revealed various kinematic parameters describing movement quality of the upper limb. However, due to the different level of stroke impairment and different assessment criteria and interventions, the evaluation of the effectiveness of rehabilitation program is undermined. This paper presents a systematic review of kinematic assessments of movement quality of the upper limb and identifies the suitable parameters describing impairments in stroke patients. A total of 41 different clinical and pilot studies on different phases of stroke recovery utilizing kinematic parameters are evaluated. Kinematic parameters describing movement accuracy are mostly reported for chronic patients with statistically significant outcomes and correlate strongly with clinical assessments. Meanwhile, parameters describing feed-forward sensorimotor control are the most frequently reported in studies on sub-acute patients with significant outcomes albeit without correlation to any clinical assessments. However, lack of measures in coordinated movement and proximal component of upper limb enunciate the difficulties to distinguish the exploitation of joint redundancies exhibited by stroke patients in completing the movement. A further study on overall measures of coordinated movement is recommended.

Musculoskeletal models have been widely used for detailed biomechanical analysis to characterise various functional impairments given their ability to estimate movement variables (i.e., muscle forces and joint moments) which cannot be readily measured in vivo. Physics-based computational neuromusculoskeletal models can interpret the dynamic interaction between neural drive to muscles, muscle dynamics, body and joint kinematics and kinetics. Still, such set of solutions suffers from slowness, especially for the complex models, hindering the utility in real-time applications. In recent years, data-driven methods have emerged as a promising alternative due to the benefits in speedy and simple implementation, but they cannot reflect the underlying neuromechanical processes. This paper proposes a physics-informed deep learning framework for musculoskeletal modelling, where physics-based domain knowledge is brought into the data-driven model as soft constraints to penalise/regularise the data-driven model. We use the synchronous muscle forces and joint kinematics prediction from surface electromyogram (sEMG) as the exemplar to illustrate the proposed framework. Convolutional neural network (CNN) is employed as the deep neural network to implement the proposed framework. Simultaneously, the physics law between muscle forces and joint kinematics is used the soft constraint. Experimental validations on two groups of data, including one benchmark dataset and one self-collected dataset from six healthy subjects, are performed. The experimental results demonstrate the effectiveness and robustness of the proposed framework.

This paper presents the development of a novel adaptive wearable ankle robot for the treatments of ankle sprain through physical rehabilitation. The ankle robot has a bioinspired design, devised after a careful study of the improvement opportunities in the existing ankle robots. Robot design is adaptable to subjects of varying physiological abilities and age groups. Ankle robot employs lightweight but powerful pneumatic muscle actuators (PMA) which mimics skeletal muscles in actuation. To address nonlinear characteristics of PMA, a fuzzy-based disturbance observer (FBDO) has been developed. Another instance of an adaptive fuzzy logic controller based on Mamdani inference has been developed and appended with the FBDO to compensate for the transient nature of the PMA. With the proposed control scheme, it is possible to simultaneously control four parallel actuators of the ankle robot and achieve three rotational degrees of freedom. To evaluate the robot design, the disturbance observer, and the adaptive fuzzy logic controller, experiments were performed. The ankle robot was used by a neurologically intact subject. The robot-human interaction was kept as active-passive while the robot was operated on predefined trajectories commonly adopted by the therapists. Trajectory tracking results are reported in the presence of an unpredicted human user intervention, use of compliant and nonlinear actuators, and parallel kinematic structure of the ankle robot.

Intervention of robotic devices in the field of physical gait therapy can help in providing repetitive, systematic, and economically viable training sessions. Interactive or assist-as-needed (AAN) gait training encourages patient voluntary participation in the robotic gait training process which may aid in rapid motor function recovery. In this paper, a lightweight robotic gait training orthosis with two actuated and four passive degrees of freedom (DOFs) is proposed. The actuated DOFs were powered by pneumatic muscle actuators. An AAN gait training paradigm based on adaptive impedance control was developed to provide interactive robotic gait training. The proposed adaptive impedance control scheme adapts the robotic assistance according to the disability level and voluntary participation of human subjects. The robotic orthosis was operated in two gait training modes, namely, inactive mode and active mode, to evaluate the performance of the proposed control scheme. The adaptive impedance control scheme was evaluated on ten neurologically intact subjects. The experimental results demonstrate that an increase in voluntary participation of human subjects resulted in a decrease of the robotic assistance and vice versa. Further clinical evaluations with neurologically impaired subjects are required to establish the therapeutic efficacy of the adaptive-impedance-control-based AAN gait training strategy.