2022
Tsinganos, Panagiotis; Jansen, Bart; Cornelis, Jan; Skodras, Athanassios
Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks Journal Article
In: Sensors, vol. 22, no. 5, pp. 1694, 2022.
Abstract | Links | BibTeX | Tags: attention, CNN, Deep learning, hand gesture recognition, real time, semg, TCN
@article{Tsinganos2022,
title = {Real-Time Analysis of Hand Gesture Recognition with Temporal Convolutional Networks},
author = {Panagiotis Tsinganos and Bart Jansen and Jan Cornelis and Athanassios Skodras},
editor = {Antonio Fernández-Caballero and Juan M. Corchado},
url = {https://www.mdpi.com/1424-8220/22/5/1694},
doi = {10.3390/s22051694},
year = {2022},
date = {2022-02-22},
urldate = {2022-02-22},
journal = {Sensors},
volume = {22},
number = {5},
pages = {1694},
abstract = {In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.},
keywords = {attention, CNN, Deep learning, hand gesture recognition, real time, semg, TCN},
pubstate = {published},
tppubtype = {article}
}
In recent years, the successful application of Deep Learning methods to classification problems has had a huge impact in many domains. (1) Background: In biomedical engineering, the problem of gesture recognition based on electromyography is often addressed as an image classification problem using Convolutional Neural Networks. Recently, a specific class of these models called Temporal Convolutional Networks (TCNs) has been successfully applied to this task. (2) Methods: In this paper, we approach electromyography-based hand gesture recognition as a sequence classification problem using TCNs. Specifically, we investigate the real-time behavior of our previous TCN model by performing a simulation experiment on a recorded sEMG dataset. (3) Results: The proposed network trained with data augmentation yields a small improvement in accuracy compared to our existing model. However, the classification accuracy is decreased in the real-time evaluation, showing that the proposed TCN architecture is not suitable for such applications. (4) Conclusions: The real-time analysis helps in understanding the limitations of the model and exploring new ways to improve its performance.