Authors:
Nuno Pires
1
and
Milton P. Macedo
2
;
1
Affiliations:
1
Instituto Politécnico de Coimbra, Rua da Misericórdia, Lagar dos Cortiços, S. Martinho do Bispo, 3045-093 Coimbra, Portugal
;
2
LIBPhys, Department of Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal
Keyword(s):
Bionic Hand, Electromyography, Force Myography, Feature Extraction, Gesture Recognition, Classification Models.
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. Prelimina
ry 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.
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