Hands are vital in a wide range of fundamental daily activities, and neurological diseases
that impede hand function can significantly affect quality of life. Wearable hand gesture …

Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …

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 …

As physiological signals that are closely related to human motion, surface electromyography
(sEMG) signals have been widely used in human-robot interaction systems (HRISs). Some …

Abstract Recently, Convolutional Neural Networks (CNNs) have been used for the
classification of hand activities from surface Electromyography (sEMG) signals. However …

Gesture recognition technology is widely used in the flexible and precise control of
manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform …

This paper presents an effective transfer learning (TL) strategy for the realization of surface
electromyography (sEMG)-based gesture recognition with high generalization and low …

Gesture recognition using sparse multichannel surface electromyography (sEMG) is a
challenging problem, and the solutions are far from optimal from the point of view of muscle …

Pattern recognition of electromyography (EMG) signals can potentially improve the
performance of myoelectric control for upper limb prostheses with respect to current clinical …

Designing efficient and labor-saving prosthetic hands requires powerful hand gesture
recognition algorithms that can achieve high accuracy with limited complexity and latency. In …