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A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States
Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in reha...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513020/ https://www.ncbi.nlm.nih.gov/pubmed/34640722 http://dx.doi.org/10.3390/s21196401 |
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author | Pinto-Bernal, Maria J. Cifuentes, Carlos A. Perdomo, Oscar Rincón-Roncancio, Monica Múnera, Marcela |
author_facet | Pinto-Bernal, Maria J. Cifuentes, Carlos A. Perdomo, Oscar Rincón-Roncancio, Monica Múnera, Marcela |
author_sort | Pinto-Bernal, Maria J. |
collection | PubMed |
description | Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%. |
format | Online Article Text |
id | pubmed-8513020 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-85130202021-10-14 A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States Pinto-Bernal, Maria J. Cifuentes, Carlos A. Perdomo, Oscar Rincón-Roncancio, Monica Múnera, Marcela Sensors (Basel) Article Physical exercise contributes to the success of rehabilitation programs and rehabilitation processes assisted through social robots. However, the amount and intensity of exercise needed to obtain positive results are unknown. Several considerations must be kept in mind for its implementation in rehabilitation, as monitoring of patients’ intensity, which is essential to avoid extreme fatigue conditions, may cause physical and physiological complications. The use of machine learning models has been implemented in fatigue management, but is limited in practice due to the lack of understanding of how an individual’s performance deteriorates with fatigue; this can vary based on physical exercise, environment, and the individual’s characteristics. As a first step, this paper lays the foundation for a data analytic approach to managing fatigue in walking tasks. The proposed framework establishes the criteria for a feature and machine learning algorithm selection for fatigue management, classifying four fatigue diagnoses states. Based on the proposed framework and the classifier implemented, the random forest model presented the best performance with an average accuracy of ≥98% and F-score of ≥93%. This model was comprised of ≤16 features. In addition, the prediction performance was analyzed by limiting the sensors used from four IMUs to two or even one IMU with an overall performance of ≥88%. MDPI 2021-09-25 /pmc/articles/PMC8513020/ /pubmed/34640722 http://dx.doi.org/10.3390/s21196401 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pinto-Bernal, Maria J. Cifuentes, Carlos A. Perdomo, Oscar Rincón-Roncancio, Monica Múnera, Marcela A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States |
title | A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States |
title_full | A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States |
title_fullStr | A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States |
title_full_unstemmed | A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States |
title_short | A Data-Driven Approach to Physical Fatigue Management Using Wearable Sensors to Classify Four Diagnostic Fatigue States |
title_sort | data-driven approach to physical fatigue management using wearable sensors to classify four diagnostic fatigue states |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513020/ https://www.ncbi.nlm.nih.gov/pubmed/34640722 http://dx.doi.org/10.3390/s21196401 |
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