Novel and Efficient Hyperdimensional Encoding of Surface Electromyography Signals for Hand Gesture Recognition

Ancelin Salerno, Sylvain Barraud

2025

Abstract

Gesture recognition has become a crucial component of human-computer interaction, with applications ranging from virtual reality to assistive technologies. This study explores Hyperdimensional Computing (HDC) as a powerful alternative to traditional machine learning techniques for real-time gesture recognition. HDC is known for its robustness and efficiency, enabling fast and accurate classification though the use of high-dimensional binary vectors. In this study, we introduce two key variants aimed at significantly improving the performance of gesture recognition: (1) an enhancement of item memory representation enabling a better gestures recognition, and (2) an advanced temporal encoding mechanism that captures the dynamic nature of gestures more efficiently. These modifications are evaluated using a benchmark dataset of surface electromyography (sEMG) signals, demonstrating significant improvements in both accuracy and computational efficiency.

Paper Citation

in Harvard Style

Salerno A. and Barraud S. (2025). Novel and Efficient Hyperdimensional Encoding of Surface Electromyography Signals for Hand Gesture Recognition. 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 763-772. DOI: 10.5220/0013257500003911

in Bibtex Style

@conference{biosignals25,
author={Ancelin Salerno and Sylvain Barraud},
title={Novel and Efficient Hyperdimensional Encoding of Surface Electromyography Signals for Hand Gesture Recognition},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOSIGNALS},
year={2025},
pages={763-772},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013257500003911},
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 - Novel and Efficient Hyperdimensional Encoding of Surface Electromyography Signals for Hand Gesture Recognition
SN - 978-989-758-731-3
AU - Salerno A.
AU - Barraud S.
PY - 2025
SP - 763
EP - 772
DO - 10.5220/0013257500003911
PB - SciTePress