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 language | English |
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Title of host publication | 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings |
Publisher | IEEE |
Pages | 1105-1109 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 13 Sep 2018 |
Event | IEEE International Conference on Acoustics, Speech and Signal Processing - Calgary, Calgary, Canada Duration: 15 Jan 2018 → 20 Jan 2018 Conference number: 2018 https://2018.ieeeicassp.org/ https://2018.ieeeicassp.org |
Publication series
Name | IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP): Proceedings |
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Publisher | IEEE |
ISSN (Electronic) | 2379-190X |
Conference
Conference | IEEE International Conference on Acoustics, Speech and Signal Processing |
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Abbreviated title | ICASSP |
Country/Territory | Canada |
City | Calgary |
Period | 15/01/2018 → 20/01/2018 |
Internet address |
Keywords
- Electromyographic (EMG), time-domain features, pattern recognition, linear discriminant analysis (LDA), multilayer perceptron (MLP).