2021
Tsinganos, Panagiotis; Cornelis, Jan; Cornelis, Bruno; Jansen, Bart; Skodras, Athanassios
Transfer Learning in sEMG-based Gesture Recognition Proceedings Article
In: 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA), IEEE, Chania, Crete, Greece, 2021.
Abstract | Links | BibTeX | Tags: Deep learning, hand gesture recognition, semg, transfer learning
@inproceedings{Tsinganos_et_al_transfer2021,
title = {Transfer Learning in sEMG-based Gesture Recognition},
author = {Panagiotis Tsinganos and Jan Cornelis and Bruno Cornelis and Bart Jansen and Athanassios Skodras},
url = {https://ieeexplore.ieee.org/abstract/document/9555555},
doi = {10.1109/IISA52424.2021.9555555},
year = {2021},
date = {2021-10-08},
urldate = {2021-10-08},
booktitle = {2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)},
publisher = {IEEE},
address = {Chania, Crete, Greece},
abstract = {The latest advancements in the field of deep learning and biomedical engineering have allowed for the development of myoelectric interfaces based on deep neural networks. A longstanding problem of these interfaces is that the models cannot easily be applied to new users due to the high variability and stochastic nature of the electromyography signals. Further training a new model for every new subject requires the collection of large volumes of data. Therefore, this work proposes a transfer learning (TL) scheme which allows reusing the knowledge of a pre-existing model for a new user. Firstly, a convolutional neural network (CNN) is trained on an initial dataset using the data of multiple subjects. Then, the weights of this model are fine-tuned for a new target subject. The approach is evaluated on the Ninapro datasets DB2 and DB7. The experimentation included three different CNN models and eight preprocessing alternatives. The results showed that the success of the TL method depends on how the data are preprocessed. Specifically, the biggest accuracy improvement (+5.14%) is achieved when only the first 20% of the signal duration is used.},
keywords = {Deep learning, hand gesture recognition, semg, transfer learning},
pubstate = {published},
tppubtype = {inproceedings}
}
The latest advancements in the field of deep learning and biomedical engineering have allowed for the development of myoelectric interfaces based on deep neural networks. A longstanding problem of these interfaces is that the models cannot easily be applied to new users due to the high variability and stochastic nature of the electromyography signals. Further training a new model for every new subject requires the collection of large volumes of data. Therefore, this work proposes a transfer learning (TL) scheme which allows reusing the knowledge of a pre-existing model for a new user. Firstly, a convolutional neural network (CNN) is trained on an initial dataset using the data of multiple subjects. Then, the weights of this model are fine-tuned for a new target subject. The approach is evaluated on the Ninapro datasets DB2 and DB7. The experimentation included three different CNN models and eight preprocessing alternatives. The results showed that the success of the TL method depends on how the data are preprocessed. Specifically, the biggest accuracy improvement (+5.14%) is achieved when only the first 20% of the signal duration is used.