Muscle activity detection from myoelectric signals based on the AR-GARCH model

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Myoelectric (EMG) signals contain temporal muscle activation information, that is essential in understanding and diagnosing neuromuscular disorders. Given the biological stochasticity and measurement noise, statistical signal processing methods are adopted in the literature to detect the muscle activity onset and offset periods. However, these methods carry an implicit assumption of stationarity. In this paper, we show that the EMG signal is non-stationary and the nature of its non-stationarity is reminiscent of the heteroscedasticity, i.e., the conditional variance of the signal is time-varying. We therefore model the EMG signal using an Autoregressive-Generalized Autoregressive Conditional Heteroscedastic (AR-GARCH) process, which captures the heteroscedasticity of the signal. The Akaike information criterion test confirms that the AR-GARCH model better fits the EMG signal than the stationary AR model. We subsequently propose a muscle activity detector that relies on the estimated conditional variance of the AR-GARCH model. The application of the proposed detector to real EMG signal shows that the proposed AR-GARCH-based detector achieves a higher accuracy than the widely used double threshold detector.

Original languageEnglish (US)
Title of host publication2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Pages420-423
Number of pages4
DOIs
StatePublished - Nov 6 2012
Externally publishedYes
Event2012 IEEE Statistical Signal Processing Workshop, SSP 2012 - Ann Arbor, MI, United States
Duration: Aug 5 2012Aug 8 2012

Other

Other2012 IEEE Statistical Signal Processing Workshop, SSP 2012
Country/TerritoryUnited States
CityAnn Arbor, MI
Period8/5/128/8/12

All Science Journal Classification (ASJC) codes

  • Signal Processing

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