Transfer-Modal Extraction of Surface EMG Features for Upper Limb Motor Classification

Vedant Mangrulkar, Madhav Rao

2025

Abstract

Surface Electromyography (sEMG) signals provide critical insights into muscular activity, aiding action classification and monitoring muscular disorders. However, their reliability is hindered by noise and unstructured data. Despite the advancements in machine learning, large datasets are essential to address these challenges and enhance decoding accuracy for further development. Hence, this work attempts to predict the sEMG features from the accelerometer signals in a view to generate synthetic data which is useful for further developments around this physiological signal. This work examines the correlation between accelerometer-generated sEMG features and those from original sEMG signals for four upper limb actions wrist flexion, wrist extension, wrist closing and wrist vibration focusing on the flexor carpi ulnaris and extensor carpi radialis muscles. Synthesized features are augmented with original features to train an ML model, achieving 91% accuracy on unseen original sEMG features. This work showcases a viable solution to generate more sEMG features corresponding to the actions under test from an altogether different modality. This work is a step towards synthesizing EMG signals and features for human limb movements which offers a strong platform to design imitation learning for rehabilitation systems in the future.

Paper Citation

in Harvard Style

Mangrulkar V. and Rao M. (2025). Transfer-Modal Extraction of Surface EMG Features for Upper Limb Motor Classification. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS; ISBN 978-989-758-731-3, SciTePress, pages 721-728. DOI: 10.5220/0013157800003911

in Bibtex Style

@conference{biosignals25,
author={Vedant Mangrulkar and Madhav Rao},
title={Transfer-Modal Extraction of Surface EMG Features for Upper Limb Motor Classification},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2025},
pages={721-728},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013157800003911},
isbn={978-989-758-731-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS
TI - Transfer-Modal Extraction of Surface EMG Features for Upper Limb Motor Classification
SN - 978-989-758-731-3
AU - Mangrulkar V.
AU - Rao M.
PY - 2025
SP - 721
EP - 728
DO - 10.5220/0013157800003911
PB - SciTePress