Gesture Recognition Through the Implementation of a Bimodal Acquisition System Using EMG and FMG Signals

Nuno Pires, Milton P. Macedo, Milton P. Macedo

2025

Abstract

This study is part of a broader project, the Open Source Bionic Hand, which aims to develop and control, in real time, a low-cost 3D-printed bionic hand prototype using signals from the muscles of the forearm. In this work, it is intended to implement a bimodal signal acquisition system, which uses EMG signals and Force Myography (FMG), in order to optimize the recognition of gesture intention and, consequently, the control of the bionic hand. The implementation of this bimodal EMG/FMG system will be described. It uses two different signals from BITalino EMG modules and Flexiforce™ sensors from Tekscan™. The dataset was built from thirty-six features extracted from each acquisition using two of each EMG and FMG sensors in extensor and flexor muscle groups simultaneously. The extraction of features is also depicted as well as the subsequent use of these features to train and compare Machine Learning models in gesture recognition, through MATLAB's Classification Learner tool. Preliminary results obtained from a dataset of three healthy volunteers, show the effectiveness of this bimodal EMG/FMG system in the improvement of the efficacy on gesture recognition as it is shown for example for the Quadratic SVM classifier that raises from 75,00% with EMG signals to 87,96% using both signals.

Paper Citation

in Harvard Style

Pires N. and Macedo M. (2025). Gesture Recognition Through the Implementation of a Bimodal Acquisition System Using EMG and FMG Signals. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: WHC; ISBN 978-989-758-731-3, SciTePress, pages 1018-1026. DOI: 10.5220/0013401800003911

in Bibtex Style

@conference{whc25,
author={Nuno Pires and Milton Macedo},
title={Gesture Recognition Through the Implementation of a Bimodal Acquisition System Using EMG and FMG Signals},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: WHC},
year={2025},
pages={1018-1026},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013401800003911},
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 2: WHC
TI - Gesture Recognition Through the Implementation of a Bimodal Acquisition System Using EMG and FMG Signals
SN - 978-989-758-731-3
AU - Pires N.
AU - Macedo M.
PY - 2025
SP - 1018
EP - 1026
DO - 10.5220/0013401800003911
PB - SciTePress