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.
Department of Electrical and Biomedical Engineering, Vanderbilt University, Nashville, TN 37235.
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Chen
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Shiavi
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