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Current Directions in Biomedical Engineering, 8(2), 660–663p. (2022) DOI:10.1515/cdbme-2022-1168
Neuromuscular fatigue may show in surface electromyographic recordings (sEMG) by a shift in spectral features. As of now, it is unclear whether other parameters may influence reported fatigue-detection methods as well. Artificial test signals were constructed matching the spectrum of real sEMGs with evolving fatigue from one-minute long segments of eight subjects' forearms. Frequency based method mean frequency (MNF), spectral moments ratio (SMR5), waveletbased WIRM as well as non-linear fatigue indicators sample entropy (SampEn), fuzzy approximate entropy (fApEn) and recurrence quantification analysis percent determinism (RQA%DET) were included. Pearson correlation of fatigued signals from sEMG and corresponding artificial segments and their quadratic error were used to quantify deviations. Furthermore, separability of fatiguing and non-fatigued situations was evaluated. Frequency-based methods exhibited high correlations and low squared errors implying that there is hardly any difference between fatigue detection in sEMG and corresponding artificial segments. Non-linear entropy methods diverged from this observation: SampEn yielded reduced correlation, elevated squared error and poorer separability. However, fApEn results were close to frequency methods. Although some non-linear fatigue detection methods seem to incorporate non-linear fatigue features in addition to spectral changes, these have limited influence to the overall performance. Our study suggests that fatigue detection methods based on spectral properties are of maximal relevance.
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