Human activity recognition (HAR) has become increasingly popular in recent years due to its
potential to meet the growing needs of various industries. Electromyography (EMG) is …

Transformer architectures have become increasingly popular in healthcare applications.
Through outstanding performance in natural language processing and superior capability to …

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

Thanks to the powerful capability of the feature extraction, deep learning has become a
promising technology for an increasing number of researchers to decode movements from …

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 …

One of the key problems in lower limb-based human–computer interaction (HCI) technology
is to use wearable devices to recognize the wearer's lower limb motions. The information …

Estimation of hand kinematics from surface electromyographic (sEMG) signals provides a
non-invasive human-machine interface. This approach is usually subject-specific, so that the …

With the development of signal analysis technology and artificial intelligence, surface
electromyography (sEMG) signal gesture recognition is widely used in rehabilitation therapy …

Though the forearm is the focus of the prostheses, myoelectric control with the electrodes on
the wrist is more comfortable for general consumers because of its unobtrusiveness and …

Recent silent speech recognition (SSR) studies based on surface electromyography (sEMG)
have been conducted by classifying a finite number of words or phrases without sufficient …