Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
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Other documents of the author: Ramos, Guilherme; Vaz, J.R.; Mendonça, G.V.; Pezarat-Correia, P.; Rodrigues, J.; Alfaras, Miquel; Gamboa, Hugo
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Title
Fatigue Evaluation through Machine Learning and a Global Fatigue DescriptorAuthor (s)
Date
2020-01Publisher
HindawiISSN
2040-2295Bibliographic citation
G. Ramos, J. R. Vaz, G. V. Mendonça, P. Pezarat-Correia, J. Rodrigues, M. Alfaras, H. Gamboa, "Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor", Journal of Healthcare Engineering, vol. 2020, Article ID 6484129, 18 pages, 2020. https://doi.org/10.1155/2020/6484129Type
info:eu-repo/semantics/articlePublisher version
https://www.hindawi.com/journals/jhe/2020/6484129/Version
info:eu-repo/semantics/publishedVersionAbstract
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 ... [+]
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. )erefore, 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. )e 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. [-]
Is part of
Journal of Healthcare Engineering, vol. 2020Investigation project
The acquired data were collected within the projected PTDC/DTP-DES/5714/2014-Contralateral effects of low intensity resistance training combined with blood flow restriction, funded by Fundação para a Ciencia e Tecnologia (FCT), and J. R. Vaz was supported by ˆ NIH-P20GM109090 and by the University of Nebraska at Omaha Office of Research and Creative Activity. João Rodrigues participates in “iNOVA4Health-Programme in Translational Medicine” with a FCT grant I&D 2015-2020, while Guilherme Ramos was supported in the first phase by AHA CMUP-ERI/HCI/0046. )e authors acknowledge the support that Miquel Alfaras received from ITN AffecTech, under the Marie Skłodowska Curie Actions (ERC H2020 Project ID: 722022)Rights
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Except where otherwise noted, this item's license is described as Copyright © 2020 G. Ramos et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited