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).
|Title of host publication||Advances in Information and Communication - Proceedings of the 2021 Future of Information and Communication Conference, FICC|
|Number of pages||17|
|Publication status||Published - 16 Apr 2021|
|Event||Future of Information and Communication Conference, FICC 2021 - Virtual, Online|
Duration: 29 Apr 2021 → 30 Apr 2021
|Name||Advances in Intelligent Systems and Computing|
|Conference||Future of Information and Communication Conference, FICC 2021|
|Period||29/04/21 → 30/04/21|
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
- Machine learning
- Real-time gesture classification