Based on the structure and motion bionic principle of the normal adult fingers, biological characteristics of human hands were analyzed, and a wearable exoskeleton hand function training device for the rehabilitation of stroke patients or patients with hand trauma was designed. This device includes the exoskeleton mechanical structure and the electromyography (EMG) control system. With adjustable mechanism, the device was capable to fit different finger lengths, and by capturing the EMG of the users’ contralateral limb, the motion state of the exoskeleton hand was controlled. Then driven by the device， the user’s fingers conducting adduction/abduction rehabilitation training was carried out. Finally, the mechanical properties and training effect of the exoskeleton hand were verified through mechanism simulation and the experiments on the experimental prototype of the wearable exoskeleton hand function training device.
In order to help the patients with upper-limb disfunction go on rehabilitation training, this paper proposed an upper-limb exoskeleton rehabilitation robot with four degrees of freedom (DOF), and realized two control schemes, i.e., voice control and electromyography control. The hardware and software design of the voice control system was completed based on RSC-4128 chips, which realized the speech recognition technology of a specific person. Besides, this study adapted self-made surface eletromyogram (sEMG) signal extraction electrodes to collect sEMG signals and realized pattern recognition by conducting sEMG signals processing, extracting time domain features and fixed threshold algorithm. In addition, the pulse-width modulation(PWM)algorithm was used to realize the speed adjustment of the system. Voice control and electromyography control experiments were then carried out, and the results showed that the mean recognition rate of the voice control and electromyography control reached 93.1% and 90.9%, respectively. The results proved the feasibility of the control system. This study is expected to lay a theoretical foundation for the further improvement of the control system of the upper-limb rehabilitation robot.