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

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.