Temporal feature extraction and clustering analysis of electromyographic linear envelopes in gait studies

A technique for automatically clustering linear envelopes of EMGs (electromyograms) during gait has been developed. It uses a temporal feature representation and a maximum peak matching scheme. This 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 number 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.<
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