Recent technological advancements in data acquisition tools allowed life scientists to
acquire multimodal data from different biological application domains. Categorized in three …

Background and objective: We have cast the net into the ocean of knowledge to retrieve the
latest scientific research on deep learning methods for physiological signals. We found 53 …

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

Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …

The surface electromyography (sEMG)-based gesture recognition with deep learning
approach plays an increasingly important role in human-computer interaction. Existing deep …

Objective. Advanced robotic lower limb prostheses are mainly controlled autonomously.
Although the existing control can assist cyclic movements during locomotion of amputee …

Electromyography (EMG) has already been broadly used in human-machine interaction
(HMI) applications. Determining how to decode the information inside EMG signals robustly …

Amputation of the upper limb brings heavy burden to amputees, reduces their quality of life,
and limits their performance in activities of daily life. The realization of natural control for …

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

Hand prostheses controlled by surface electromyography are promising due to the non-
invasive approach and the control capabilities offered by machine learning. Nevertheless …