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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.
|Title of host publication||2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||4|
|Publication status||Published - 27 Aug 2020|
|Event||42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Montréal, Québec, Canada|
Duration: 20 Jul 2020 → 24 Jul 2020
Conference number: 42
|Conference||42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society|
|Abbreviated title||EMBC 2020|
|Period||20/07/20 → 24/07/20|
FingerprintDive into the research topics of 'Automatic Myoelectric Control Site Detection Using Candid Covariance-Free Incremental Principal Component Analysis'. Together they form a unique fingerprint.
- 1 Finished
Sensorimotor learning for control of prosthetic limbs
1/02/18 → 31/01/23