EMG-Based Hand Gesture Classification with Long Short-Term Memory Deep Recurrent Neural Networks

Milad Jabbari, Rami N. Khushaba, Kianoush Nazarpour

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Electromyogram (EMG) pattern recognition has been utilized with the traditional machine and deep learning architectures as a control strategy for upper-limb prostheses. However, most of these learning architectures, including those in convolutional neural networks, focus the spatial correlations only; but muscle contractions have a strong temporal dependency. Our primary aim in this paper is to investigate the effectiveness of recurrent deep learning networks in EMG classification as they can learn long-term and non-linear dynamics of time series. We used a Long Short-Term Memory (LSTM-based) neural network to perform multiclass classification with six grip gestures at three different force levels (low, medium, and high) generated by nine amputees. Four different feature sets were extracted from the raw signals and fed to LSTM. Moreover, to investigate a generalization of the proposed method, three different training approaches were tested including 1) training the network with feature extracted from one specific force level and testing it with the same force level, 2) training the network with one specific force level and testing it with two remained force levels, and 3) training the network with all of the force levels and testing it with a single force level. Our results show that LSTM-based neural network can provide reliable performance with average classification errors of around 9% across all nine amputees and force levels. We demonstrate the applicability of deep learning for upperlimb prosthesis control.
Original languageEnglish
Title of host publication2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages3302-3305
Number of pages4
ISBN (Electronic)978-1-7281-1990-8
ISBN (Print)978-1-7281-1991-5
DOIs
Publication statusPublished - 27 Aug 2020
Event42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Montréal, Québec, Canada
Duration: 20 Jul 202024 Jul 2020
Conference number: 42
https://embc.embs.org/2020/

Publication series

Name
PublisherIEEE
ISSN (Print)1557-170X
ISSN (Electronic)1558-4615

Conference

Conference42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Abbreviated titleEMBC 2020
CountryCanada
CityQuébec
Period20/07/2024/07/20
Internet address

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