2021
Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios
The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals Journal Article
In: International Journal of Electrical and Computer Engineering Systems, vol. 12, no. 1, pp. 23 - 31, 2021, ISSN: 1847-6996.
Abstract | Links | BibTeX | Tags: classification, CNN, Deep learning, electromyography, hand gesture recognition, hilbert curve, Peano curve, semg, space-filling curve, Z-order curve
@article{Tsinganos2021,
title = {The Effect of Space-filling Curves on the Efficiency of Hand Gesture Recognition Based on sEMG Signals},
author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},
url = {https://ijeces.ferit.hr/index.php/ijeces/article/view/345},
doi = {10.32985/ijeces.12.1.3},
issn = {1847-6996},
year = {2021},
date = {2021-04-21},
journal = {International Journal of Electrical and Computer Engineering Systems},
volume = {12},
number = {1},
pages = {23 - 31},
abstract = {Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano- and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.},
keywords = {classification, CNN, Deep learning, electromyography, hand gesture recognition, hilbert curve, Peano curve, semg, space-filling curve, Z-order curve},
pubstate = {published},
tppubtype = {article}
}
2020
Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios
Data Augmentation of Surface Electromyography for Hand Gesture Recognition Journal Article
In: Sensors, vol. 20, no. 17, pp. 4892, 2020, ISSN: 1424-8220.
Abstract | Links | BibTeX | Tags: CNN, data augmentation, Deep learning, electromyography, hand gesture recognition, semg
@article{Tsinganos2020b,
title = {Data Augmentation of Surface Electromyography for Hand Gesture Recognition},
author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},
doi = {10.3390/s20174892},
issn = {1424-8220},
year = {2020},
date = {2020-08-29},
journal = {Sensors},
volume = {20},
number = {17},
pages = {4892},
abstract = {The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.},
keywords = {CNN, data augmentation, Deep learning, electromyography, hand gesture recognition, semg},
pubstate = {published},
tppubtype = {article}
}
Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios
Hilbert sEMG data scanning for hand gesture recognition based on deep learning Journal Article
In: Neural Computing and Applications, 2020, ISBN: 1433-3058.
Abstract | Links | BibTeX | Tags: classification, CNN, Deep learning, electromyography, hand gesture recognition, hilbert curve, Multi-scale, semg
@article{Tsinganos2020,
title = {Hilbert sEMG data scanning for hand gesture recognition based on deep learning},
author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},
url = {https://github.com/DSIP-UPatras/sEMG-hilbert-curve},
doi = {10.1007/s00521-020-05128-7},
isbn = {1433-3058},
year = {2020},
date = {2020-07-07},
journal = {Neural Computing and Applications},
abstract = {Deep learning has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed toward surface electromyography (sEMG)-based gesture recognition, often addressed as an image classification problem using convolutional neural networks (CNNs). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals, which allows the application of typical image processing pipelines such as CNNs on sequence data. The proposed method is evaluated on different state-of-the-art network architectures and yields a significant classification improvement over the approach without the Hilbert curve. Additionally, we develop a new network architecture (MSHilbNet) that takes advantage of multiple scales of an initial Hilbert curve representation and achieves equal performance with fewer convolutional layers.},
keywords = {classification, CNN, Deep learning, electromyography, hand gesture recognition, hilbert curve, Multi-scale, semg},
pubstate = {published},
tppubtype = {article}
}
2019
Tsinganos, Panagiotis; Cornelis, Bruno; Cornelis, Jan; Jansen, Bart; Skodras, Athanassios
A Hilbert Curve Based Representation of sEMG Signals for Gesture Recognition Proceedings Article
In: 2019 International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 201–206, IEEE, Osijek, Croatia, 2019, ISBN: 978-1-7281-3253-2.
Links | BibTeX | Tags: classification, CNN, Deep learning, electromyography, hand gesture recognition, hilbert curve, semg
@inproceedings{Tsinganos2019b,
title = {A Hilbert Curve Based Representation of sEMG Signals for Gesture Recognition},
author = {Panagiotis Tsinganos and Bruno Cornelis and Jan Cornelis and Bart Jansen and Athanassios Skodras},
url = {https://ieeexplore.ieee.org/document/8787290/},
doi = {10.1109/IWSSIP.2019.8787290},
isbn = {978-1-7281-3253-2},
year = {2019},
date = {2019-06-01},
booktitle = {2019 International Conference on Systems, Signals and Image Processing (IWSSIP)},
pages = {201--206},
publisher = {IEEE},
address = {Osijek, Croatia},
keywords = {classification, CNN, Deep learning, electromyography, hand gesture recognition, hilbert curve, semg},
pubstate = {published},
tppubtype = {inproceedings}
}