Hand gesture-based systems are one of the most effective technological advances and
continue to develop with improvements in the field of human–computer interaction. Surface …

This work is motivated by the recent advances in Deep Neural Networks (DNNs) and their
widespread applications in human-machine interfaces. DNNs have been recently used for …

Hand gesture recognition systems are gaining popularity these days due to the ease with
which humans and machines can communicate. The goal of hand gesture development is to …

Recently, human–machine interfaces (HMI) that make life convenient have been studied in
many fields. In particular, a hand gesture recognition (HGR) system, which can be …

This letter investigates the feasibility of a generalizable solution for human-robot interfaces
through peripheral multichannel Electromyography (EMG) recording. We propose a …

Recently, there has been a surge of significant interest on application of Deep Learning (DL)
models to autonomously perform hand gesture recognition using surface Electromyogram …

Advances in biosignal signal processing and machine learning, in particular Deep Neural
Networks (DNNs), have paved the way for the development of innovative Human-Machine …

Electromyography (EMG) is one of the most common methods to detect muscle activities and
intentions. However, it has been difficult to estimate accurate hand motions represented by …

This work is motivated by the recent advancements of Deep Neural Networks (DNNs) for
myoelectric prosthesis control. In this regard, hand gesture recognition via surface …

In the field of surface electromyography (sEMG) gesture recognition, how to improve
recognition accuracy has been a research hotspot. The rapid development of deep learning …