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

Christos Dolopikos*, Michael Pritchard, Jordan J. Bird, Diego R. Faria

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication


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).

Original languageEnglish
Title of host publicationAdvances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC
EditorsKohei Arai
Number of pages17
ISBN (Electronic)9783030731038
ISBN (Print)9783030731021
Publication statusPublished - 16 Apr 2021
EventFuture of Information and Communication Conference, FICC 2021 - Virtual, Online
Duration: 29 Apr 202130 Apr 2021

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1364 AISC
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365


ConferenceFuture of Information and Communication Conference, FICC 2021
CityVirtual, Online

Bibliographical note

© 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.


  • Biosignal processing
  • Deep learning
  • EMG
  • Machine learning
  • Real-time gesture classification


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