Improved Gesture Recognition Based on sEMG Signals and TCN
Panagiotis Tsinganos, Bruno Cornelis, Jan Cornelis, Bart Jansen, Athanassios Skodras
Abstract
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using Temporal Convolutional Networks. The proposed network yields an improvement in gesture recognition of almost 5% to the state of the art reported in the literature, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.
BibTeX
@inproceedings{icassp2019_improvedgesturer,
title = {Improved Gesture Recognition Based on sEMG Signals and TCN},
author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},
booktitle = {ICASSP 2019},
year = {2019}
}