Mental fatigue is a leading cause of motor vehicle accidents, medical errors, loss of
workplace productivity, and student disengagements in e-learning environment …

Delivering health care at home emerged as a key advancement to reduce healthcare costs
and infection risks, as during the SARS-Cov2 pandemic. In particular, in motor training …

This paper presents a methodology for automatically detecting muscular activity by
denoising, extracting features, and classifying surface electromyography (sEMG) signals …

This research focuses on the gait phase recognition using different sEMG and EEG features.
Seven healthy volunteers, 23-26 years old, were enrolled in this experiment. Seven phases …

Electroencephalography (EEG) and surface electromyography (sEMG) have been widely
used in the rehabilitation training of motor function. However, EEG signals have poor user …

In brain-computer interface and neuroscience, electroencephalography (EEG) signals have
been well studied with not only cognitive activities but also physical activities. This work …

We demonstrate improved performance in the classification of bioelectric data for use in
systems such as robotic prosthesis control, by data fusion using low-cost electromyography …

Whether someone is born with a missing limb or an amputation occurs later in life, living with
this disability can be extremely challenging. The robotic prosthetic devices available today …

Hand trajectory estimation plays an important part in various fields, such as human-
computer interaction (HCI), rehabilitation robotics, and prosthetics. This study investigates …

Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable
mechatronic rehabilitation devices have been proposed for treatment. However, before …