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In order to assist the patients with hemiplegia to carry out multi-mode elbow rehabilitation training, a soft elbow rehabilitation exoskeleton robot was developed. In addition, a coordination control strategy was proposed based on human muscle torque estimation and adaptive neural network compensation. The surface electromyogram signals were used to identify the motion intention of human and adjust the trajectory of rehabilitation training. The closed-loop control stability of the control algorithm was proved by Lyapunov method. A real-time control experimental platform was established to conduct the trajectory tracking experiments and free active training experiments based on human motion intention. The experimental results show that the proposed control strategy may guarantee the trajectory tracking accuracy in passive training processes. Moreover, the movement trajectory in active training processes may be adjusted based on the motion intention of patients and realize active rehabilitation training with different intensities. [ABSTRACT FROM AUTHOR]

The applications of robotics to the rehabilitation training of neuromuscular impairments have received increasing attention due to their promising prospects. The effectiveness of robot-assisted training directly depends on the control strategy applied in the therapy program. This paper presents an upper extremity exoskeleton for the functional recovery training of disabled patients. A minimal-intervention-based admittance control strategy is developed to induce the active participation of patients and maximize the use of recovered motor functions during training. The proposed control strategy can transit among three control modes, including human-conduct mode, robot-assist mode, and motion-restricted mode, based on the real-time position tracking errors of the end-effector. The human–robot interaction in different working areas can be modulated according to the motion intention of patient. Graphical guidance developed in Unity-3-D environment is introduced to provide visual training instructions. Furthermore, to improve training performance, the controller parameters should be adjusted in accordance with the hemiplegia degree of patients. For the patients with severe paralysis, robotic assistance should be increased to guarantee the accomplishment of training. For the patients recovering parts of motor functions, robotic assistance should be reduced to enhance the training intensity of effected limb and improve therapeutic effectiveness. The feasibility and effectiveness of the proposed control scheme are validated via training experiments with two healthy subjects and six stroke patients with different degrees of hemiplegia. [ABSTRACT FROM AUTHOR]

Robot-assisted training is a promising technology in clinical rehabilitation providing effective treatment to the patients with motor disability. In this paper, a multi-modal control strategy for a therapeutic upper limb exoskeleton is proposed to assist the disabled persons perform patient-passive training and patient-cooperative training. A comprehensive overview of the exoskeleton with seven actuated degrees of freedom is introduced. The dynamic modeling and parameters identification strategies of the human-robot interaction system are analyzed. Moreover, an adaptive sliding mode controller with disturbance observer (ASMCDO) is developed to ensure the position control accuracy in patient-passive training. A cascade-proportional-integral-derivative (CPID)-based impedance controller with graphical game-like interface is designed to improve interaction compliance and motivate the active participation of patients in patient-cooperative training. Three typical experiments are conducted to verify the feasibility of the proposed control strategy, including the trajectory tracking experiments, the trajectory tracking experiments with impedance adjustment, and the intention-based training experiments. The experimental results suggest that the tracking error of ASMCDO controller is smaller than that of terminal sliding mode controller. By optimally changing the impedance parameters of CPID-based impedance controller, the training intensity can be adjusted to meet the requirement of different patients. [ABSTRACT FROM AUTHOR]

• A soft wearable exoskeleton with compliance tendon-sheath actuation is developed for elbow assistance. • A neural-network-enhanced torque estimation controller is proposed to improve assistance efficiency. • The effectiveness is validated by dumbbell-lifting experiments with various weights and frequencies. • The results are compared with proportional control strategy and sEMG-based assistive control strategy. Soft wearable exoskeletons are a new approach for the applications of power assistance and rehabilitation training. In the present work, a neural-network-enhanced torque estimation controller (NNETEC) is proposed for a soft wearable elbow assistance exoskeleton with compliant tendon-sheath actuator. A comprehensive overview for the major components of the soft exoskeleton is introduced. The locations of anchor points are optimized via the maximum-stiffness principle. The NNETEC strategy is developed by fusing the feedback signals from surface electromyography (sEMG) sensors, inertial measurement units, force sensors, and motor encoder. It consists of a joint torque estimation module to identify the elbow torque of wearer based on Kalman filter, a neural-network adjustment module to recognize human motion intention, and a proportional-integral-derivative controller with hybrid position/torque feedbacks. Further experimental investigations are carried out by five volunteers to validate the effectiveness of the proposed soft elbow exoskeleton and control strategy. The results of the dumbbell-lifting experiments with various weights and frequencies demonstrate that, when compared with the proportional control strategy and the sEMG-based assistive control strategy without neural-network adjustment, the developed NNETEC method can achieve higher power assistance efficiency. [ABSTRACT FROM AUTHOR]

Abstract Motor dysfunction has become a serious threat to the health of older people and the patients with neuromuscular impairment. The application of exoskeleton to motion assistance has received increasing attention due to its promising prospects. The major contribution of this paper is to develop a joint torque estimation control strategy for a soft elbow exoskeleton to provide effective power assistance. The surface electromyography signal (sEMG) from biceps is utilized to estimate the motion intension of wearer and map into the real-time elbow joint torque. Moreover, the control strategy fusing the estimated joint torque, estimated joint angle from inertial measurement unit and encoder feedback signal is proposed to improve motion assistance performance. Finally, further experimental investigations are carried out to compare the control effectiveness of the proposed intention-based control strategy to that of the proportional control strategy. The experimental results indicate that the proposed control strategy provides better performance in elbow assistance with different loads, and the average efficiency of assistance with heavy load is about 42.66%. Highlights • A soft elbow exoskeleton suit is developed to provide effective power assistance. • A sEMG-based elbow joint torque estimation control strategy is proposed. • The proposed control strategy is more efficient and stable than proportional strategy. • The average efficiency of power assistance with heavy load is about 42.66%. [ABSTRACT FROM AUTHOR]

The patients of paralysis with motion impairment problems require extensive rehabilitation programs to regain motor functions. The great labor intensity and limited therapeutic effect of traditional human-based manual treatment have recently boosted the development of robot-assisted rehabilitation therapy. In the present work, a neural-fuzzy adaptive controller (NFAC) based on radial basis function network (RBFN) is developed for a rehabilitation exoskeleton to provide human arm movement assistance. A comprehensive overview is presented to describe the mechanical structure and electrical real-time control system of the therapeutic robot, which provides seven actuated degrees of freedom (DOFs) and achieves natural ranges of upper extremity movement. For the purpose of supporting the disable patients to perform repetitive passive rehabilitation training, the RBFN-based NFAC algorithm is proposed to guarantee trajectory tracking accuracy with parametric uncertainties and environmental disturbances. The stability of the proposed control scheme is demonstrated through Lyapunov stability theory. Further experimental investigation, involving the position tracking experiment and the frequency response experiment, are conducted to compare the control performance of the proposed method to those of cascaded proportional-integral-derivative controller (CPID) and fuzzy sliding mode controller (FSMC). The comparison results indicate that the proposed RBFN-based NFAC algorithm is capable of obtaining lower position tracking error and better frequency response characteristic. [ABSTRACT FROM AUTHOR]