Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise

Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients’ con...

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Autores:: Aguirre, Andrés
Pinto, Maria J.
Cifuentes, Carlos A.
Perdomo, Oscar
Díaz, Camilo A
Múnera, Marcela

Tipo de recurso:: Article of journal

Fecha de publicación:: 2021

Institución:: Escuela Colombiana de Ingeniería Julio Garavito

Repositorio:: Repositorio Institucional ECI

Idioma:: eng

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network_acronym_str	ESCUELAIG2
network_name_str	Repositorio Institucional ECI
repository_id_str
dc.title.eng.fl_str_mv	Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise
title	Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise
spellingShingle	Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise Ejercicios terapéuticos Exercise therapy Frecuencia cardiaca Heart rate Tecnología de Kinect Kinect technology Sensores biomédicos Biomedical sensors Estimación de fatiga Aprendizaje automático Ejercicio físico Rehabilitación física Fatigue estimation Kinect Machine learning physical exercise Physical rehabilitation
title_short	Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise
title_full	Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise
title_fullStr	Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise
title_full_unstemmed	Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise
title_sort	Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise
dc.creator.fl_str_mv	Aguirre, Andrés Pinto, Maria J. Cifuentes, Carlos A. Perdomo, Oscar Díaz, Camilo A Múnera, Marcela
dc.contributor.author.none.fl_str_mv	Aguirre, Andrés Pinto, Maria J. Cifuentes, Carlos A. Perdomo, Oscar Díaz, Camilo A Múnera, Marcela
dc.contributor.researchgroup.spa.fl_str_mv	GiBiome
dc.subject.armarc.none.fl_str_mv	Ejercicios terapéuticos Exercise therapy Frecuencia cardiaca Heart rate Tecnología de Kinect Kinect technology Sensores biomédicos Biomedical sensors
topic	Ejercicios terapéuticos Exercise therapy Frecuencia cardiaca Heart rate Tecnología de Kinect Kinect technology Sensores biomédicos Biomedical sensors Estimación de fatiga Aprendizaje automático Ejercicio físico Rehabilitación física Fatigue estimation Kinect Machine learning physical exercise Physical rehabilitation
dc.subject.proposal.spa.fl_str_mv	Estimación de fatiga Aprendizaje automático Ejercicio físico Rehabilitación física
dc.subject.proposal.eng.fl_str_mv	Fatigue estimation Kinect Machine learning physical exercise Physical rehabilitation
description	Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients’ condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject’s physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-07
dc.date.accessioned.none.fl_str_mv	2024-09-03T16:31:42Z
dc.date.available.none.fl_str_mv	2024-09-03T16:31:42Z
dc.type.spa.fl_str_mv	Artículo de revista
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversion.fl_str_mv	http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/publishedVersion
dc.type.coar.spa.fl_str_mv	http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv	Text
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/article
format	http://purl.org/coar/resource_type/c_6501
status_str	publishedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.escuelaing.edu.co/handle/001/3242
dc.identifier.eissn.spa.fl_str_mv	1424-8220
dc.identifier.instname.spa.fl_str_mv	Universidad Escuela Colombiana de Ingeniería
dc.identifier.reponame.spa.fl_str_mv	Repositorio Digital
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.escuelaing.edu.co/
url	https://repositorio.escuelaing.edu.co/handle/001/3242 https://repositorio.escuelaing.edu.co/
identifier_str_mv	1424-8220 Universidad Escuela Colombiana de Ingeniería Repositorio Digital
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.citationedition.spa.fl_str_mv	Vol. 21 No. 5006, 2021
dc.relation.citationendpage.spa.fl_str_mv	31
dc.relation.citationissue.spa.fl_str_mv	5006
dc.relation.citationstartpage.spa.fl_str_mv	1
dc.relation.citationvolume.spa.fl_str_mv	21
dc.relation.ispartofjournal.eng.fl_str_mv	Sensors
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spelling	Aguirre, Andrésb7c23972f4b7842df2d2f60ffa18a65ePinto, Maria J.a8b692e8da4a8ca7ec18eeb623dc947fCifuentes, Carlos A.0b885a45437175ae12e5d0a6f598afc4Perdomo, Oscarff88a6a3395dc44ade411d38bf28c565Díaz, Camilo A01cc93cfd973e684ae5aaea45f072009Múnera, Marcela8047a30ff2499f8ae5a4e903871b8f95GiBiome2024-09-03T16:31:42Z2024-09-03T16:31:42Z2021-07https://repositorio.escuelaing.edu.co/handle/001/32421424-8220Universidad Escuela Colombiana de IngenieríaRepositorio Digitalhttps://repositorio.escuelaing.edu.co/Physical exercise (PE) has become an essential tool for different rehabilitation programs. High-intensity exercises (HIEs) have been demonstrated to provide better results in general health conditions, compared with low and moderate-intensity exercises. In this context, monitoring of a patients’ condition is essential to avoid extreme fatigue conditions, which may cause physical and physiological complications. Different methods have been proposed for fatigue estimation, such as: monitoring the subject’s physiological parameters and subjective scales. However, there is still a need for practical procedures that provide an objective estimation, especially for HIEs. In this work, considering that the sit-to-stand (STS) exercise is one of the most implemented in physical rehabilitation, a computational model for estimating fatigue during this exercise is proposed. A study with 60 healthy volunteers was carried out to obtain a data set to develop and evaluate the proposed model. According to the literature, this model estimates three fatigue conditions (low, moderate, and high) by monitoring 32 STS kinematic features and the heart rate from a set of ambulatory sensors (Kinect and Zephyr sensors). Results show that a random forest model composed of 60 sub-classifiers presented an accuracy of 82.5% in the classification task. Moreover, results suggest that the movement of the upper body part is the most relevant feature for fatigue estimation. Movements of the lower body and the heart rate also contribute to essential information for identifying the fatigue condition. This work presents a promising tool for physical rehabilitation.El ejercicio físico (EF) se ha convertido en una herramienta imprescindible para diferentes programas de rehabilitación. Se ha demostrado que los ejercicios de alta intensidad (HIE) proporcionan mejores resultados en condiciones generales de salud, en comparación con los ejercicios de intensidad baja y moderada. En este contexto, el seguimiento del estado del paciente es fundamental para evitar condiciones extremas de fatiga, que pueden provocar complicaciones físicas y fisiológicas. Se han propuesto diferentes métodos para la estimación de la fatiga, tales como: monitoreo de parámetros fisiológicos del sujeto y escalas subjetivas. Sin embargo, todavía se necesitan procedimientos prácticos que proporcionen una estimación objetiva, especialmente para las HIE. En este trabajo, considerando que el ejercicio sit-to-stand (STS) es uno de los más implementados en rehabilitación física, se propone un modelo computacional para estimar la fatiga durante este ejercicio. Se llevó a cabo un estudio con 60 voluntarios sanos para obtener un conjunto de datos para desarrollar y evaluar el modelo propuesto. Según la literatura, este modelo estima tres condiciones de fatiga (baja, moderada y alta) mediante el monitoreo de 32 características cinemáticas STS y la frecuencia cardíaca de un conjunto de sensores ambulatorios (sensores Kinect y Zephyr). Los resultados muestran que un modelo de bosque aleatorio compuesto por 60 subclasificadores presentó una precisión del 82,5% en la tarea de clasificación. Además, los resultados sugieren que el movimiento de la parte superior del cuerpo es la característica más relevante para la estimación de la fatiga. Los movimientos de la parte inferior del cuerpo y la frecuencia cardíaca también aportan información esencial para identificar el estado de fatiga. Este trabajo presenta una herramienta prometedora para la rehabilitación física.31 páginasapplication/pdfengMDPI (Multidisciplinary Digital Publishing Institute)Basel (Suiza)https://www.mdpi.com/Machine Learning Approach for Fatigue Estimation in Sit-to-Stand ExerciseArtículo de revistainfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85Vol. 21 No. 5006, 2021315006121SensorsThompson, P. 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[CrossRef]info:eu-repo/semantics/closedAccesshttp://purl.org/coar/access_right/c_14cbEjercicios terapéuticosExercise therapyFrecuencia cardiacaHeart rateTecnología de KinectKinect technologySensores biomédicosBiomedical sensorsEstimación de fatigaAprendizaje automáticoEjercicio físicoRehabilitación físicaFatigue estimationKinectMachine learningphysical exercisePhysical rehabilitationTEXTMachine Learning Approach for Fatigue Estimation in.pdf.txtMachine Learning Approach for Fatigue Estimation in.pdf.txtExtracted texttext/plain106945https://repositorio.escuelaing.edu.co/bitstream/001/3242/4/Machine%20Learning%20Approach%20for%20Fatigue%20Estimation%20in.pdf.txt69fcfa6027d1c779715563ec401ad9cfMD54metadata only accessTHUMBNAILPortada Machine Learning Approach for Fatigue Estimation in.PNGPortada Machine Learning Approach for Fatigue Estimation in.PNGimage/png262864https://repositorio.escuelaing.edu.co/bitstream/001/3242/3/Portada%20Machine%20Learning%20Approach%20for%20Fatigue%20Estimation%20in.PNGf6044e1cd41213effa819ad0bef717b0MD53open accessMachine Learning Approach for Fatigue Estimation in.pdf.jpgMachine Learning Approach for Fatigue Estimation in.pdf.jpgGenerated Thumbnailimage/jpeg15419https://repositorio.escuelaing.edu.co/bitstream/001/3242/5/Machine%20Learning%20Approach%20for%20Fatigue%20Estimation%20in.pdf.jpg332d5c85ee4551c90faa508574bc61f8MD55metadata only accessLICENSElicense.txtlicense.txttext/plain; charset=utf-81881https://repositorio.escuelaing.edu.co/bitstream/001/3242/2/license.txt5a7ca94c2e5326ee169f979d71d0f06eMD52open accessORIGINALMachine Learning Approach for Fatigue Estimation in.pdfMachine Learning Approach for Fatigue Estimation in.pdfapplication/pdf1404821https://repositorio.escuelaing.edu.co/bitstream/001/3242/1/Machine%20Learning%20Approach%20for%20Fatigue%20Estimation%20in.pdfb6c459a9ead61e8e0a4b30cfc2224267MD51metadata only access001/3242oai:repositorio.escuelaing.edu.co:001/32422024-09-04 03:01:52.744metadata only accessRepositorio Escuela Colombiana de Ingeniería Julio Garavitorepositorio.eci@escuelaing.edu.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

Machine Learning Approach for Fatigue Estimation in Sit-to-Stand Exercise

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