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
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
Tsagkas, Nikolaos; Tsinganos, Panagiotis; Skodras, Athanassios
On the Use of Deeper CNNs in Hand Gesture Recognition Based on sEMG Signals Proceedings Article
In: 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA), pp. 1-4, 2019.
Abstract | Links | BibTeX | Tags: classification, CNN, Convolutional Neural Networks, data acquisition, database, Deep learning, hand gesture recognition, semg, signal processing, surface electromyography
@inproceedings{Tsagkas2019,
title = {On the Use of Deeper CNNs in Hand Gesture Recognition Based on sEMG Signals},
author = {Nikolaos Tsagkas and Panagiotis Tsinganos and Athanassios Skodras},
doi = {10.1109/IISA.2019.8900709},
year = {2019},
date = {2019-07-01},
booktitle = {2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA)},
pages = {1-4},
abstract = {In the past few years, a great interest for the classification of hand gestures with Deep Learning methods based on surface electromyography (sEMG) signals has been developed in the scientific community. In line with latest works in the field, the objective of our work is the construction of a novel Convolutional Neural Network architecture, for the classification of hand-gestures. Our model, while avoiding overfitting, did not perform significantly better compared to a much shallower network. The results suggest that the lack of diversity in the sEMG recordings between certain hand-gestures limits the performance of the model. In addition, the classification accuracy on a database we developed using a commercial device (Myo Armband) was substantially higher (approximately 24%) than a similar benchmark dataset recorded with the same device.},
keywords = {classification, CNN, Convolutional Neural Networks, data acquisition, database, Deep learning, hand gesture recognition, semg, signal processing, surface electromyography},
pubstate = {published},
tppubtype = {inproceedings}
}
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}
}