Gesture recognition based on wearable devices is one of the vital components of human–
computer interaction systems. Compared with skeleton-based recognition in computer …

Myoelectric control has emerged as a promising approach for a wide range of applications,
including controlling limb prosthetics, teleoperating robots and enabling immersive …

A long history has passed since electromyography (EMG) signals have been explored in
human-centered robots for intuitive interaction. However, it still has a gap between scientific …

The lack of intuitive and active human–robot interaction makes it difficult to use upper-limb-
assistive devices. In this paper, we propose a novel learning-based controller that intuitively …

Human-machine interfaces frequently use electromyography (EMG) signals. Based on
previous work, feature extraction has a great deal of influence on the performance of EMG …

Objective. The use of electromyogram (EMG) signals recorded from the wrist is emerging as
a desirable input modality for human–machine interaction (HMI). Although forearm-based …

Position-aware myoelectric prosthesis controllers require long, data-intensive training
routines. Transfer Learning (TL) might reduce training burden. A TL model can be pre …

Pattern recognition (PR)-based myoelectric control systems can naturally provide
multifunctional and intuitive control of upper limb prostheses and restore lost limb function …

In recent decades, the population of people with disabilities has had an exponential growth
due to the increase in pathologies that lead to an impairment in neuromotor functioning …

Objective Although many advancements have been made on myoelectric pattern-
recognition, the control of poly-articulated upper-limb prostheses remains insufficiently …