Stroke is one of the main causes of long-term disability worldwide, placing a large burden on
individuals and society. Rehabilitation after stroke consists of an iterative process involving …

The increasing amount of data in electromyographic (EMG) signal research has greatly
increased the importance of developing advanced data analysis and machine learning …

Wearable devices that monitor muscle activity based on surface electromyography could be
of use in the development of hand gesture recognition applications. Such devices typically …

In recent years, deep learning algorithms have become increasingly more prominent for
their unparalleled ability to automatically learn discriminant features from large amounts of …

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

Specialized myoelectric sensors have been used in prosthetics for decades, but, with recent
advancements in wearable sensors, wireless communication and embedded technologies …

Gesture recognition using sparse multichannel surface electromyography (sEMG) is a
challenging problem, and the solutions are far from optimal from the point of view of muscle …

By training the deep neural network model, the hidden features in Surface
Electromyography (sEMG) signals can be extracted. The motion intention of the human can …

Pattern recognition of electromyography (EMG) signals can potentially improve the
performance of myoelectric control for upper limb prostheses with respect to current clinical …

Online gesture classification can rely on unsupervised segmentation in order to divide the
data stream into static and dynamic segments for individual classification. However, this …