Abstract
The purpose of this study was to develop an algorithm to classify muscle fatigue content in sports related scenarios. Mechanomyography (MMG) signals of the biceps muscle were recorded from thirteen subjects performing dynamic contractions until fatigue. For training and testing purposes, the signals were labeled in two classes (Non-Fatigue and Fatigue). A genetic algorithm was used to evolve a pseudo-wavelet function for optimizing the detection of muscle fatigue. Tuning of the generalized evolved pseudo-wavelet function was based on the decomposition of 70% of the conducted MMG trials. After completing 25 independent pseudo-wavelet evolution runs, the best run was selected and then tested on the remaining 30% of the data to measure the classification performance. Results show that the evolved pseudo-wavelet improved the classification rate of muscle fatigue by 4.70 percentage points to 16.61 percentage points when compared to other standard wavelet functions, giving an average correct classification of 80.63%, with statistical significance (p < 0.05).
Highlights
Studies on localized muscle fatigue have focused mainly on the decline in the force of a muscle contraction during a sustained activity [1], which results in a definition of fatigue as the inability of a muscle to continue exerting force or power
A genetic algorithm was chosen as the method to provide an optimal solution by tuning a pseudo-wavelet function for its optimal decomposition of MMG targeted in extracting muscle fatigue content
The optimal wavelet was selected by the Genetic Algorithms (GA) based on the solution representation, where it finds the improvements according to the fitness function of the final evolved population with the best Davies Bouldin Index (DBI)
Summary
Studies on localized muscle fatigue have focused mainly on the decline in the force of a muscle contraction during a sustained activity [1], which results in a definition of fatigue as the inability of a muscle to continue exerting force or power. There are various techniques to detect muscle fatigue, the most researched ones are mechanomyography (MMG) and surface electromyography (sEMG) [2]. In MMG it is the mechanical signal from the surface of a contracting muscle that is measured, i.e., when the muscle fibers move they cause vibrations which can be recorded [3]. A selection of sensors can be utilized to record MMG signals, e.g., hydrophones, condenser microphones, piezoelectric contact sensors, accelerometers, and laser distance sensors [3,4]. In studies on muscle fatigue accelerometers have been utilized to detect changes during exercise. Barry et al [5] used an accelerometer to detect changes in vibration amplitude during voluntary and evoked muscle vibrations in fatiguing muscle contractions
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