Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor
Joint Authors
Pezarat-Correia, Pedro C.
Gamboa, Hugo
Ramos, G.
Vaz, J. R.
Mendonça, G. V.
Rodrigues, J.
Alfaras, M.
Source
Journal of Healthcare Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-01-07
Country of Publication
Egypt
No. of Pages
18
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Ramos, G.& Vaz, J. R.& Mendonça, G. V.& Pezarat-Correia, Pedro C.& Rodrigues, J.& Alfaras, M.…[et al.]. 2020. Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor. Journal of Healthcare Engineering،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1186298
Modern Language Association (MLA)
Ramos, G.…[et al.]. Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor. Journal of Healthcare Engineering No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1186298
American Medical Association (AMA)
Ramos, G.& Vaz, J. R.& Mendonça, G. V.& Pezarat-Correia, Pedro C.& Rodrigues, J.& Alfaras, M.…[et al.]. Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor. Journal of Healthcare Engineering. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1186298
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1186298