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

Recently, there has been a surge of significant interest on application of Deep Learning (DL)
models to autonomously perform hand gesture recognition using surface Electromyogram …

Objective. Neural signals in residual muscles of amputated limbs are frequently decoded to
control powered prostheses. Yet myoelectric controllers assume muscle activities of residual …

By observing the actions taken by operators, it is possible to determine the risk level of a
work task. One method for achieving this is the recognition of human activity using …

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

Myoelectric hand prostheses are effective tools for upper limb amputees to regain hand
functions. Much progress has been made with pattern recognition algorithms to recognize …

In recent years, rehabilitation robots have been gradually applied to assist patients with
movement disorders to improve their motor functions. Active training, as an important …

Hand Gesture Recognition (HGR) using surface Electromyogram (sEMG) signals can be
considered as one of the most important technologies in making efficient Human Machine …

Previous research identifies that pushing and pulling is responsible for approximately 9–
18% of all low back injuries. Additionally, the handle design of a cart being pushed can …

The Calf Raise Test (CRT) is a widely used functional assessment of triceps surae muscle-
tendon unit function. While it exhibits acceptable reliability, administration inconsistencies …