For the problem of surface electromyography (sEMG) gesture recognition, considering the
fact that the traditional machine learning model is susceptible to the sEMG feature extraction …

This paper presents an effective transfer learning (TL) strategy for the realization of surface
electromyography (sEMG)-based gesture recognition with high generalization and low …

The current research on using surface electromyography (sEMG) for gesture recognition
mainly focuses on designing EMG signal features, decent feature designs can significantly …

The deep learning gesture recognition based on surface electromyography plays an
increasingly important role in human-computer interaction. In order to ensure the high …

Gesture recognition via surface electromyography (sEMG) has drawn significant attention in
the field of human–computer interaction. An important factor limiting the performance of …

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

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 …

In recent years, surface electromyography (sEMG)-based human–computer interaction has
been developed to improve the quality of life for people. Gesture recognition based on the …

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 …

In the past, investigators tend to use multi-channel surface electromyography (sEMG) signal
acquisition devices to improve the recognition accuracy for the study of gesture recognition …