Principal components analysis preprocessing to reduce controller delays in pattern recognition based myoelectric control.
Information extracted from signals recorded from multi-channel surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle; the contribution from each specific muscle being modified by a tissue filter between the muscle and the detection points. In this work, the measured raw MES signals are rotated by class specific rotation matrices to spatially decorrelate the measured data prior to feature extraction. This tunes the pattern recognition classifier to better discriminate the test motions. Using this preprocessing step, MES analysis windows may be cut from 256 ms to 128 ms without affecting the classification accuracy.
Hargrove
L
L
Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB Canada. levi.hargrove@unb.ca
Scheme
E
E
Englehart
K
K
Hudgins
B
B
D000465:Algorithms; D004576:Electromyography; D005542:Forearm / Q000502:physiology*; D006801:Humans; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted