Paper Info
Title
Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis
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.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9175614
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Simon A Stuttaford100.34
Agamemnon Krasoulis232.86
Sigrid Dupan311.70
Kianoush Nazarpour47519.08
Matthew Dyson502.70