Hand gestures are crucial for develo** prosthetic and rehabilitation devices, enabling
intuitive human–computer interaction (HCI) and improving accessibility for individuals with …

For the problem of surface electromyography (sEMG) gesture recognition, considering the
fact that the traditional machine learning model is susceptible to the sEMG feature extraction …

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 …

Quantifying movement variability is a crucial aspect for clinical and laboratory investigations
in several contexts. However, very few studies have assessed, in detail, the intra-subject …

The spread of the use of robotic devices in neuro-rehabilitation therapies requires the
availability of lightweight, easy-to-use, cost-effective and versatile systems. RobHand has …

The applications of myoelectrical interfaces are majorly limited by the efficacy of decoding
motion intent in the electromyographic (EMG) signal. Currently, EMG classification methods …

The success criteria of electromyography (EMG) rely on recognizing the pattern and
correlating it to its target, especially in the regression task, which has become a popular …

Background Shedding light on the neuroscientific mechanisms of human upper limb motor
control, in both healthy and disease conditions (eg, after a stroke), can help to devise …

Objective. Upper limb loss can profoundly impact an individual's quality of life, posing
challenges to both physical capabilities and emotional well-being. To restore limb function …

Quantitative assessment of hand function can assist therapists in providing appropriate
rehabilitation strategies, which plays an essential role in post-stroke rehabilitation …