Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor.

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ID: 88265
2020
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Abstract
Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. The system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82 ± 0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.
Reference Key
ramos2020fatiguejournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ramos, G;Vaz, J R;Mendonça, G V;Pezarat-Correia, P;Rodrigues, J;Alfaras, M;Gamboa, H;
Journal journal of healthcare engineering
Year 2020
DOI
10.1155/2020/6484129
URL
Keywords
Machine learning representation learning drug-target interaction word2vec feature embedding

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