Electromyography Signal-Based Gesture Recognition for Human-Machine Interaction in Real-Time Through Model Calibration

Abstract

In this work, we achieve up to 92% classification accuracy of electromyographic data between five gestures in pseudo-real-time. Most current state-of-the-art methods in electromyographical signal processing are unable to classify real-time data in a post-learning environment, that is, after the model is trained and results are analysed. In this work we show that a process of model calibration is able to lead models from 67.87% real-time classification accuracy to 91.93%, an increase of 24.06%. We also show that an ensemble of classical machine learning models can outperform a Deep Neural Network. An original dataset of EMG data is collected from 15 subjects for 4 gestures (Open-Fingers, Wave-Out, Wave-in, Close-fist) using a Myo Armband for measurement of forearm muscle activity. The dataset is cleaned between gesture performances on a per-subject basis and a sliding temporal window algorithm is used to perform statistical analysis of EMG signals and extract meaningful mathematical features as input to the learning paradigms. The classifiers used in this paper include a Random Forest, a Support Vector Machine, a Multilayer Perceptron, and a Deep Neural Network. The three classical classifiers are combined into a single model through an ensemble voting system which scores 91.93% compared to the Deep Neural Network which achieves a performance of 88.68%, both after calibrating to a subject and performing real-time classification (pre-calibration scores for the two being 67.87% and 74.27%, respectively).

Publication DOI: https://doi.org/10.1007/978-3-030-73103-8_65
Divisions: College of Engineering & Physical Sciences
College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Computer Science
College of Engineering & Physical Sciences > Aston Institute of Urban Technology and the Environment (ASTUTE)
College of Engineering & Physical Sciences > Systems analytics research institute (SARI)
Additional Information: © Springer Nature B.V. 2021. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-030-73103-8_65 Funding: This work is partially supported by EPSRC-UK InDex project (EU CHIST-ERA programme), with reference EP/S032355/1 and by the Royal Society (UK) through the project "Sim2Real" with grant number RGS\R2\192498.
Event Title: Future of Information and Communication Conference, FICC 2021
Event Type: Other
Event Dates: 2021-04-29 - 2021-04-30
Uncontrolled Keywords: Biosignal processing,Deep learning,EMG,Machine learning,Real-time gesture classification,Control and Systems Engineering,Computer Science(all)
ISBN: 9783030731021, 9783030731038
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... -030-73103-8_65 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2021-04-16
Accepted Date: 2021-04-01
Authors: Dolopikos, Christos
Pritchard, Michael (ORCID Profile 0000-0002-3783-0230)
Bird, Jordan J. (ORCID Profile 0000-0002-9858-1231)
Faria, Diego R. (ORCID Profile 0000-0002-2771-1713)

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 16 April 2022.


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