Among the methods of hand function rehabilitation after stroke, robot-assisted rehabilitation is widely used, and the use of hand rehabilitation robots can provide functional training of the hand or assist the paralyzed hand with activities of daily living. However, patients with hand disorders consistently report that the needs of some users are not being met. The purpose of this review is to understand the reasons why these user needs are not being adequately addressed, to explore research on hand rehabilitation robots, to review their current state of research in recent years, and to summarize future trends in the hope that it will be useful to researchers in this research area. This review summarizes the techniques in this paper in a systematic way. We first provide a comprehensive review of research institutions, commercial products, and literature. Thus, the state of the art and deficiencies of functional hand rehabilitation robots are sought and guide the development of subsequent hand rehabilitation robots. This review focuses specifically on the actuation and control of hand functional rehabilitation robots, as user needs are primarily focused on actuation and control strategies. We also review hand detection technologies and compare them with patient needs. The results show that the trends in recent years are more inclined to pursue new lightweight materials to improve hand adaptability, investigating intelligent control methods for human-robot interaction in hand functional rehabilitation robots to improve control robustness and accuracy, and VR virtual task positioning to improve the effectiveness of active rehabilitation training.

Wearable devices based on surface electromyography (sEMG) to detect muscle activity can be used to assess muscle strength with the development of hand rehabilitation applications. However, conventional acquisition devices are usually complicated to operate and poorly comfortable for more medical and scientific application scenarios. Here, we report a flexible sEMG acquisition system that combines a graphene-based flexible electrode with a signal acquisition flexible printed circuit (FPC) board. Our system utilizes a polydimethylsiloxane (PDMS) substrate combined with graphene transfer technology to develop a flexible sEMG sensor. The single-lead sEMG acquisition system was designed and the FPC board was fabricated considering the requirements of flexible bending and twisting. We demonstrate the above design approach and extend this flexible sEMG acquisition system to applications for assessing muscle strength and hand rehabilitation training using a long- and short-term memory network training model trained to predict muscle strength, with 98.81% accuracy in the test set. The device exhibited good flexion and comfort characteristics. In general, the ability to accurately and imperceptibly monitor surface electromyography (EMG) signals is critical for medical professionals and patients.

As one of the most influential symptoms of daily life after stroke, hand dysfunction has seriously affected the quality of life of patients and families. At present, the commonly used rehabilitation method is to carry out continuous passive training on the patient’s fingers with the help of physical therapists, so as to promote the rehabilitation of the hands. However, this kind of therapist-assisted rehabilitation greatly increases the cost of rehabilitation treatment and is not conducive to the promotion of household use. Many exoskeleton hand rehabilitation robots still lack in overall weight and control, resulting in the application potential of exoskeleton hand rehabilitation robots not being fully developed, and the commercial and clinical success cases are limited. The research of this paper focuses on the structural design and electronic control design of the exoskeleton hand rehabilitation robot. Through the design of an exoskeleton hand rehabilitation robot suitable for human hands, the kinematics parameters are obtained by kinematics simulation, and the lightweight design of the hand rehabilitation robot is completed by using topology optimization. At the same time, this paper shows the development technology of the rehabilitation robot control system. We hope that through the subsequent product development, the exoskeleton hand rehabilitation robot studied in this paper can be applied in the future.