Effect of fatigue on the stationarity of surface electromyography signals

- Fatigue reduces stationarity of surface electromyography signal.
- Isometric exertions exhibit higher stationarity compared to dynamic exertions.
- Window size of 512 ms shows better stationarity trends than other window sizes.

The estimation of muscle fatigue using surface electromyography (SEMG) is of high relevance to evaluate ergonomic risk factors in the occupational settings. Signal stationarity plays an important role while selecting appropriate SEMG signal processing method for fatigue evaluation. The Fourier algorithm based signal processing methods (mean or median frequency of power spectrum) rely on the assumption that the signal under investigation is stationary. Stationarity of SEMG signals and its association with fatigue is rarely studied in the ergonomics literature. Therefore, this study was aimed at understanding the effect of fatigue on the stationarity of the SEMG data. Ten participants performed 40 min of fatiguing upper extremity exertions and SEMG data were recorded from the right upper trapezius muscle. The SEMG data recorded under static and dynamic conditions at the beginning and at the end of fatiguing exertions were used in the analysis. The stationarity analysis was performed for five window sizes of 128, 256, 512, 768 and 1024 ms using modified reverse arrangement test. The results showed that the muscle fatigue reduced the stationarity of the SEMG signal under static and dynamic conditions. The relationship between the muscle fatigue and the stationarity of the SEMG signal was found to be significant at the window size of 512 ms. A significantly higher fatigue related decrease in the stationarity was observed during dynamic exertions compared to the static exertions.Relevance to industryThe findings from the current study illustrate that the stationarity of SEMG signals could be used to quantify muscle fatigue under static and dynamic task conditions. These findings are useful to the ergonomic practitioners in conducting muscle fatigue estimation using SEMG.