Temporal feature extraction and clustering analysis of electromyographic linear envelopes in gait studies.
A technique for automatically clustering linear envelopes of the EMG during gait has been developed which uses a temporal feature representation and a maximum peak matching scheme. This new technique provides a viable way to define compact and meaningful EMG waveform features. The envelope matching is performed by dynamic programming, providing qualitatively the largest numbers of matched peaks and quantitatively a minimum distance measurement. The resulting averaged EMG profiles have low statistical variation and can serve as templates for EMG comparison and further classification.
Chen
J J
JJ
Department of Electrical and Biomedical Engineering, Vanderbilt University, Nashville, TN 37235.
Shiavi
R
R
D000465:Algorithms; D002547:Cerebral Palsy / Q000503:physiopathology*; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted; D004576:Electromyography; D005684:Gait / Q000502:physiology*; D006801:Humans; D012016:Reference Values; D013997:Time Factors