Machine learning (ML) has seen impressive growth in health science research due to its
capacity for handling complex data to perform a range of tasks, including unsupervised …

Muscle electrophysiology has emerged as a powerful tool to drive human machine
interfaces, with many new recent applications outside the traditional clinical domains, such …

In this work, we present a hardware-software solution to improve the robustness of hand
gesture recognition to confounding factors in myoelectric control. The solution includes a …

Myoelectric pattern recognition (MPR) has evolved into a sophisticated technology widely
employed in controlling myoelectric interface (MI) devices like prosthetic and orthotic robots …

Synthetic data augmentation is of paramount importance for machine learning classification,
particularly for biological data, which tend to be high dimensional and with a scarcity of …

Surface electromyography (sEMG) is a non-invasive technique that measures the electrical
activity generated by the muscles using sensors placed on the skin. It has been widely used …

Surface electromyography (sEMG)-based gesture classification methods have been widely
developed in neural decoding. However, these decoding methods are usually constrained …

Objective. Surface electromyography (sEMG) is a non-invasive technique that records the
electrical signals generated by muscles through electrodes placed on the skin. sEMG is the …

Objective: Surface electromyography (sEMG) can sense the motor commands transmitted to
the muscles. This work presents a deep learning method that can decode the …

Recently, the research community has shown significant interest in the continuous temporal
data obtained from motion sensors in wearable devices. These data are useful for …