Surface electromyography (sEMG) is a promising technology that can capture muscle
activation signals to control robots through novel human–machine interfaces (HMIs). This …

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

Surface electromyography (EMG) is one of the promising signals for the recognition of the
intended hand movement of an amputee. Nevertheless, there are several barriers to its …

Over the last few years, there has been significant growth in neurological diseases which
drastically affect a person's ability to perform everyday tasks, reducing their overall well …

The effectiveness of knee rehabilitation systems in aiding patients with rehabilitation training
has been well-documented. Presently, there is an increasing emphasis on the wearing …

Myoelectric control has gained much attention which translates the human intentions into
control commands for exoskeletons. The electromyogram (EMG)-driven musculoskeletal …

Human arm exhibits superb maneuverability in performing various tasks by using its ability
to actively adjust impedance parameters and interaction forces; therefore, identifying the …

The existing surface electromyography-based pattern recognition system (sEMG-PRS)
exhibits limited generalizability in practical applications. In this paper, we propose a stacked …

In order to solve the problems of single movement pattern recognition information and low
recognition accuracy of multijoint upper limb exoskeleton rehabilitation training, a …

This study introduces a novel approach for real-time hand gesture classification through the
integration of a Frequency-based Attention Neural Network (FANN) with Subject-Adaptive …