Multifractal analysis of sEMG signals for fatigue assessment in dynamic contractions using Hurst exponents

Multifractal analysis are useful to characterize complex physiological time-series. In this work, surface EMG signals recorded from biceps brachii muscles of 30 subjects are analyzed in dynamic fatigue conditions using multifractal techniques. The signals are segmented into six zones for time normalization. The first and last zones are considered as nonfatigue and fatigue conditions. The preprocessed signals are subjected to multifractal analysis and Hurst exponent function is computed. Three features, namely maximum and minimum exponent and strength of multifractality are used for analyzing nonfatigue and fatigue regions. The results indicate strength of multifractality is very high in fatigue condition and highly significant (p>2.7E-6) as compared to nonfatigue condition. The multifractal Hurst features are found to be useful in analyzing sEMG signal characteristics and this work can be extended for studying neuromuscular conditions.