What are the best indicators of myoelectric manifestation of fatigue?

Abstract

The myoelectric manifestation of fatigue (MMF) is predominantly assessed using median frequency and amplitude of electromyographic (EMG) signals. However, EMG has complex features so that fractals, correlation, entropy, and chaos MMF indicators were introduced to detect alteration of EMG features caused by muscle fatigue that may not be detected by linear indicators. The aim of this study was to determine the best MMF indicators. Twenty-four participants were equipped with EMG sensors on 9 shoulder muscles and performed a repetitive pointing task. They reported their rate of perceived exertion every 30 seconds and were stopped when they reached 8 or higher on the CR10 Borg scale. Partial least square regression was used to predict perceived exertion through 15 MMF indicators. In addition, the proportion of participants with a significant change between task initiation and termination was determined for each MMF indicator and muscle. The PLSR model explained 73% of the perceived exertion variance. Median frequency, mobility, spectral entropy, fuzzy entropy, and Higuchi fractal dimension had the greatest importance to predict perceived exertion and changed for 83.5% participants on average between task initiation and termination for the anterior and medial deltoids. The amplitude, activity, approximate, sample, and multiscale entropy, degree of multifractality, percent determinism and recurrent, correlation dimension, and largest Lyapunov exponent analysis MMF indicators were not efficient to assess MMF. Mobility, spectral entropy, fuzzy entropy, and Higuchi fractal dimension should be further considered to assess muscle fatigue and their combination with median frequency may further improve the assessment of muscle fatigue.

A novel stretching and folding characterization method based on geometrical and physiological traits of chaotic and intermittent tracking signals

“…The accurate and precise results were attained by combining Higuchi's FD and other nonlinear features [54,58]. The combination of Higuchi's FD and CD was employed for better evaluation of muscle fatigue [77], or better differentiation of multilevel emotions [58], breathing rate [78], and disease states in multiple sclerosis, Alzheimer's and Parkinson's disease [79]. Consequently, in this study, both FD and CD were used for better investigation and comparison of tracking dynamics among different tracking tasks.…”