Gesture recognition via surface electromyography (sEMG) has drawn significant attention in
the field of human–computer interaction. An important factor limiting the performance of …

Affective touch plays an important role in human-robot interaction. However, it is challenging
for robots to perceive various natural human tactile gestures accurately, and feedback …

Objective. Surface electromyography pattern recognition (sEMG-PR) is considered as a
promising control method for human-machine interaction systems. However, the …

This paper explores a new online learning problem where the data streams are generated
from an over-time varying feature space, in which the random variables are of mixed data …

Collaborative robots are an integral component of the intelligent manufacturing field. The
recognition of hand motions based on surface electromyography signals is even more …

Machine learning on electromyography (EMG) has recently achieved remarkable success
on various tasks, while such success relies heavily on the assumption that the training and …

Learning with data streams has recently been the focus of extensive research and various
solutions have been proposed. However, most such studies assume that the features remain …

Feature selection, an essential technique in data mining, is often confined to batch learning
or online idealization of data scenarios despite its significance. Existing online feature …

Real-time Hand Gesture Recognition (HGR) has emerged as a vital technology in human-
computer interaction, offering intuitive and natural ways for users to interact with computer …

In recent years, the number of people with disabilities has increased hugely, especially in
low-and middle-income countries. At the same time, robotics has made significant advances …