Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis

Simon A. Stuttaford, Agamemnon Krasoulis, Sigrid S.G. Dupan, Kianoush Nazarpour, Matthew Dyson

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

Abstract

The unknown composition of residual muscles surrounding the stump of an amputee makes optimal electrode placement challenging. This often causes the experimental set-up and calibration of upper-limb prostheses to be time consuming. In this work, we propose the use of existing dimensionality reduction techniques, typically used for muscle synergy analysis, to provide meaningful real-time functional information of the residual muscles during the calibration period. Two variations of principal component analysis (PCA) were applied to electromyography (EMG) data collected during a myoelectric task. Candid covariance-free incremental PCA (CCIPCA) detected task-specific muscle synergies with high accuracy using minimal amounts of data. Our findings offer a real-time solution towards optimizing calibration periods.
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)
Pages3497-3500
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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