EMG burst presence probability: A joint time-frequency representation of muscle activity and its application to onset detection

Jie Liu*, Dongwen Ying, William Z Rymer

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

The purpose of this study was to quantify muscle activity in the time-frequency domain, therefore providing an alternative tool to measure muscle activity. This paper presents a novel method to measure muscle activity by utilizing EMG burst presence probability (EBPP) in the time-frequency domain. The EMG signal is grouped into several Mel-scale subbands, and the logarithmic power sequence is extracted from each subband. Each log-power sequence can be regarded as a dynamic process that transits between the states of EMG burst and non-burst. The hidden Markov model (HMM) was employed to elaborate this dynamic process since HMM is intrinsically advantageous in modeling the temporal correlation of EMG burst/non-burst presence. The EBPP was eventually yielded by HMM based on the criterion of maximum likelihood. Our approach achieved comparable performance with the Bonato method.

Original languageEnglish (US)
Pages (from-to)1193-1197
Number of pages5
JournalJournal of Biomechanics
Volume48
Issue number6
DOIs
StatePublished - Apr 13 2015

Keywords

  • EMG burst presence probability (EBPP)
  • EMG onset
  • Electromyography (EMG)
  • Hidden Markov model (HMM)

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Rehabilitation
  • Biophysics
  • Biomedical Engineering
  • Medicine(all)

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