EMGTFNet: Fuzzy Vision Transformer to decode Upperlimb sEMG signals for Hand Gestures Recognition

TL;DR

This paper introduces EMGTFNet, a novel fuzzy vision transformer architecture that effectively recognizes hand gestures from sEMG signals, achieving high accuracy without extensive data augmentation or large models.

Contribution

The paper presents EMGTFNet, a new ViT-based model with a Fuzzy Neural Block that improves gesture recognition accuracy and efficiency over existing models.

Findings

Achieved 83.57% accuracy on NinaPro dataset.

Outperformed ViT without FNB, demonstrating the benefit of FNB.

Operates with only 56,793 parameters, suitable for practical applications.

Abstract

Myoelectric control is an area of electromyography of increasing interest nowadays, particularly in applications such as Hand Gesture Recognition (HGR) for bionic prostheses. Today's focus is on pattern recognition using Machine Learning and, more recently, Deep Learning methods. Despite achieving good results on sparse sEMG signals, the latter models typically require large datasets and training times. Furthermore, due to the nature of stochastic sEMG signals, traditional models fail to generalize samples for atypical or noisy values. In this paper, we propose the design of a Vision Transformer (ViT) based architecture with a Fuzzy Neural Block (FNB) called EMGTFNet to perform Hand Gesture Recognition from surface electromyography (sEMG) signals. The proposed EMGTFNet architecture can accurately classify a variety of hand gestures without any need for data augmentation techniques, transfer learning or a significant increase in the number of parameters in the network. The accuracy of the proposed model is tested using the publicly available NinaPro database consisting of 49 different hand gestures. Experiments yield an average test accuracy of 83.57\% \& 3.5\% using a 200 ms window size and only 56,793 trainable parameters. Our results outperform the ViT without FNB, thus demonstrating that including FNB improves its performance. Our proposal framework EMGTFNet reported the significant potential for its practical application for prosthetic control.