Myoelectric control for prosthetic hands is an important topic in the field of rehabilitation. Intuitive and intelligent myoelectric control can help amputees to regain upper limb function. However, current research efforts are primarily focused on developing rich myoelectric classifiers and biomimetic control methods, limiting prosthetic hand manipulation to simple grasping and releasing tasks, while rarely exploring complex daily tasks. In this article, we conduct a systematic review of recent achievements in two areas, namely, intention recognition research and control strategy research. Specifically, we focus on advanced methods for motion intention types, discrete motion classification, continuous motion estimation, unidirectional control, feedback control, and shared control. In addition, based on the above review, we analyze the challenges and opportunities for research directions of functionality-augmented prosthetic hands and user burden reduction, which can help overcome the limitations of current myoelectric control research and provide development prospects for future research.

Teleoperation in soft robotics can endow soft robots with the ability to perform complex tasks through human–robot interaction. In this study, we propose a teleoperated anthropomorphic soft robot hand with variable degrees of freedom (DOFs) and a metamorphic palm. The soft robot hand consists of four pneumatic-actuated fingers, which can be heated to tune stiffness. A metamorphic mechanism was actuated to morph the hand palm by servo motors. The human fingers' DOF, gesture, and muscle stiffness were collected and mapped to the soft robotic hand through the sensory feedback from surface electromyography devices on the jib. The results show that the proposed soft robot hand can generate a variety of anthropomorphic configurations and can be remotely controlled to perform complex tasks such as primitively operating the cell phone and placing the building blocks. We also show that the soft hand can grasp a target through the slit by varying the DOFs and stiffness in a trail.

Gestures recognition based on surface electromyography (sEMG) has been widely used for human-computer interaction. However, there are few research studies on overcoming the influence of physiological factors among different individuals. In this paper, a cross-individual gesture recognition method based on long short-term memory (LSTM) networks is proposed, named cross-individual LSTM (CI-LSTM). CI-LSTM has a dual-network structure, including a gesture recognition module and an individual recognition module. By designing the loss function, the individual information recognition module assists the gesture recognition module to train, which tends to orthogonalize the gesture features and individual features to minimize the impact of individual information differences on gesture recognition. Through cross-individual gesture recognition experiments, it is verified that compared with other selected algorithm models, the recognition accuracy obtained by using the CI-LSTM model can be improved by an average of 9.15%. Compared with other models, CI-LSTM can overcome the influence of individual characteristics and complete the task of cross-individual hand gestures recognition. Based on the proposed model, online control of the prosthetic hand is realized.

Individuals with upper limb amputation face significant difficulty in operating standard computer peripherals with their prostheses, particularly the mouse, due to the constraints imposed by current prosthetic hand designs and the irregular shape of the computer mouse. This work introduces a soft myoelectric prosthesis system for operating different physical computer mouses. The proposed prosthesis has two tendon‐driven soft fingers and three fin‐ray fingers. It enables an enveloping grasp of the computer mouse and facilitates diverse finger‐based clicking and scrolling operations. In addition, a mapping between the hand movements (myoelectric signals) and the computer mouse operations is created by combining a neural network classifier and a post‐processing algorithm. Herein, the proposed system's performance is evaluated through a series of cursor control tasks involving both the able‐bodied and amputee participants. In the experimental results, it is shown that the proposed soft prosthesis system effectively enables individuals with transradial amputation to operate a physical computer mouse using their residual limbs. Compared to a traditional cursor control method based on surface electromyography, the participants exhibit improved task performance (shorter completion time and higher throughput) and improved user experience (reduced muscle use and higher questionnaire scores).