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 …

Existing multi-view classification algorithms focus on promoting accuracy by exploiting
different views, typically integrating them into common representations for follow-up tasks …

Human–computer interaction (HCI) and related technologies focus on the implementation of
interactive computational systems. The studies in HCI emphasize on system use, creation of …

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

Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …

This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their
widespread applications in human-machine interfaces. DNNs have been recently used for …

Deep learning methods have been widely used for the classification of hand gestures using
sEMG signals. Existing deep learning architectures only captures local spatial information …

Gesture recognition is one of the important ways of human‐computer interaction, which is
mainly detected by visual technology. The temporal and spatial features are extracted by …

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 …