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}
}
2019
Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios
Improved Gesture Recognition Based on sEMG Signals and TCN Proceedings Article
In: ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1169–1173, IEEE, Brighton, UK, 2019, ISBN: 978-1-4799-8131-1.
Abstract | Links | BibTeX | Tags: CNN, Deep learning, Gesture Recognition, semg, TCN
@inproceedings{Tsinganos2019c,
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},
url = {https://ieeexplore.ieee.org/document/8683239/},
doi = {10.1109/ICASSP.2019.8683239},
isbn = {978-1-4799-8131-1},
year = {2019},
date = {2019-05-01},
booktitle = {ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
pages = {1169--1173},
publisher = {IEEE},
address = {Brighton, UK},
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.},
keywords = {CNN, Deep learning, Gesture Recognition, semg, TCN},
pubstate = {published},
tppubtype = {inproceedings}
}