Gesture Recognition Using Surface Electromyography and Deep Learning for Prostheses Hand: State-of-the-Art, Challenges, and Future

Abstract

Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life, and limits their performance in activities of daily life. The realization of natural control for prosthetic hands is crucial to improving the quality of life of amputees. Surface electromyography (sEMG) signal is one of the most widely used biological signals for the prediction of upper limb motor intention, which is an essential element of the control systems of prosthetic hands. The conversion of sEMG signals into effective control signals often requires a lot of computational power and complex process. Existing commercial prosthetic hands can only provide natural control for very few active degrees of freedom. Deep learning (DL) has performed surprisingly well in the development of intelligent systems in recent years. The significant improvement of hardware equipment and the continuous emergence of large data sets of sEMG have also boosted the DL research in sEMG signal processing. DL can effectively improve the accuracy of sEMG pattern recognition and reduce the influence of interference factors. This paper analyzes the applicability and efficiency of DL in sEMG-based gesture recognition and reviews the key techniques of DL-based sEMG pattern recognition for the prosthetic hand, including signal acquisition, signal preprocessing, feature extraction, classification of patterns, post-processing, and performance evaluation. Finally, the current challenges and future prospects in clinical application of these techniques are outlined and discussed.

A Critical Review on Hand Gesture Recognition using sEMG: Challenges, Application, Process and Techniques

“…Surface EMGs uses sensors to assess the muscle activities and is used to elicit hand motions. The intrinsic muscle movements are recorded by sEMG sensor from the skin surface whenever skeletal muscles are stimulated by the physiological brain activity and hence represents the formation and dissemination of compound nerve impulses, that are the coordinated action of motor units [11]. The data gathered from the sEMG sensors is then used to interpret the motion in limbs and detect hand gestures.…”

Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey

“…EMG can be recorded noninvasively by employing skin-mounted electrodes (the so-called surface EMG (sEMG), which is by far the most used option) or invasively using implanted electrodes into the muscle (enabling more specific information on neural control) [44,60,86]. Currently, sEMG is the most widely used signal in biomedical HMI applications for prosthetic control [26][27][28]33]. It is easy to access and provides an intuitive control strategy to reproduce the function of a biological limb.…”

Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning–A Survey in Myoelectric Control

“…Xiong et al [25] summarised the developments and applications of DL in both PR and SPC schemes. Meanwhile, Li et al [26] reviewed the key techniques in each procedure of DL-based gesture/movement recognition.…”