EMG Acquisition and Hand Pose Classification for Bionic Hands from Randomly-placed Sensors

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2 Citations (Scopus)
262 Downloads (Pure)

Abstract

This paper presents a unique real-time motion recognition system for Electromyographic (EMG) signal acquisition and classification. It is the first approach which can classify hand poses from multi-channel EMG signals gathered from randomly placed arm sensors as accurately as current placed-sensor EMG acquisition approaches. It combines time-domain feature extraction, Linear Discriminant Analysis (LDA) feature projection and Multilayer Perceptron (MLP) classification to allow nine distinct poses to be correctly identified more than 95% of the time. This is comparable to state-of-the-art placed-sensor EMG acquisition systems. Processing times of 11.70 ms also make this a viable candidate approach for real-time EMG acquisition and processing in practical prosthesis applications.
Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings
Publisher IEEE
Pages1105-1109
Number of pages5
DOIs
Publication statusPublished - 13 Sep 2018
EventIEEE International Conference on Acoustics, Speech and Signal Processing - Calgary, Calgary, Canada
Duration: 15 Jan 201820 Jan 2018
Conference number: 2018
https://2018.ieeeicassp.org/
https://2018.ieeeicassp.org

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings
PublisherIEEE
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
Abbreviated titleICASSP
CountryCanada
CityCalgary
Period15/01/201820/01/2018
Internet address

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Keywords

  • Electromyographic (EMG), time-domain features, pattern recognition, linear discriminant analysis (LDA), multilayer perceptron (MLP).

Cite this

Raurale, S., McAllister, J., & Martinez del Rincon, J. (2018). EMG Acquisition and Hand Pose Classification for Bionic Hands from Randomly-placed Sensors. In 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings (pp. 1105-1109). (IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings). IEEE . https://doi.org/10.1109/ICASSP.2018.8462409