Temporal Feature Extraction and Clustering Analysis of Electromyographic Linear Envelopes in Gait Studies

Jia-Jin Chen, Richard Shiavi

Research output: Contribution to journalArticle

33 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)295-302
Number of pages8
JournalIEEE Transactions on Biomedical Engineering
Volume37
Issue number3
DOIs
Publication statusPublished - 1990 Jan 1

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Distance measurement
Dynamic programming
Feature extraction

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering

Cite this

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Temporal Feature Extraction and Clustering Analysis of Electromyographic Linear Envelopes in Gait Studies. / Chen, Jia-Jin; Shiavi, Richard.

In: IEEE Transactions on Biomedical Engineering, Vol. 37, No. 3, 01.01.1990, p. 295-302.

Research output: Contribution to journalArticle

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