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.