Predicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers

Jeong Bae Ko,1,&ast; Kwang Bok Kim,1,&ast; Young Sub Shin,1 Hun Han,1 Sang Kuy Han,2 Duk Young Jung,3 Jae Soo Hong1 1Digital Health Care R&D Department, Korea Institute of Industrial Technology, Cheonan, Chuncheongnam-do, South Korea; 2Robotics R&D Department, Korea Institute of Indu...

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Autores principales:	Ko JB, Kim KB, Shin YS, Han H, Han SK, Jung DY, Hong JS
Formato:	article
Lenguaje:	EN
Publicado:	Dove Medical Press 2021
Materias:	sarcopenia physical activity machine learning inertial measurement unit Geriatrics RC952-954.6
Acceso en línea:	https://doaj.org/article/e3372073a3994de7b9f19a98c620a453
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spelling	oai:doaj.org-article:e3372073a3994de7b9f19a98c620a4532021-12-02T15:17:57ZPredicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers1178-1998https://doaj.org/article/e3372073a3994de7b9f19a98c620a4532021-09-01T00:00:00Zhttps://www.dovepress.com/predicting-sarcopenia-of-female-elderly-from-physical-activity-perform-peer-reviewed-fulltext-article-CIAhttps://doaj.org/toc/1178-1998Jeong Bae Ko,1,&ast; Kwang Bok Kim,1,&ast; Young Sub Shin,1 Hun Han,1 Sang Kuy Han,2 Duk Young Jung,3 Jae Soo Hong1 1Digital Health Care R&D Department, Korea Institute of Industrial Technology, Cheonan, Chuncheongnam-do, South Korea; 2Robotics R&D Department, Korea Institute of Industrial Technology, Ansan, Gyeonggi-do, South Korea; 3Seongnam Senior Experience Complex, Eulji University, Seongnam, Gyeonggi-do, South Korea&ast;These authors contributed equally to this workCorrespondence: Jae Soo HongDigital Health Care R&D Department, Korea Institute of Industrial Technology, Cheonan, Chuncheongnam-do, South KoreaTel +82 41-589-8412Fax +82 41-589-8413Email jshong94@kitech.re.krDuk Young JungSeongnam Senior Experience Complex, Eulji University, Seongnam, Gyeonggi-do, South KoreaTel +82 41-589-8412Email dyjung@eulji.ac.krPurpose: Sarcopenia is a symptom in which muscle mass decreases due to decreasing in the number of muscle fibers and muscle cross-sectional area as aging. This study aimed to develop a machine learning classification model for predicting sarcopenia through a inertial measurement unit (IMU)-based physical performance measurement data of female elderly.Patients and Methods: Seventy-eight female subjects from an elderly population (aged: 78.8± 5.7 years) volunteered to participate in this study. To evaluate the physical performance of the elderly, the experiment conducted timed-up-and-go test (TUG) and 6-minute walk test (6mWT) with worn a single IMU. Based on literature review, 132 features were extracted from collected data. Feature selection was performed through the Kruskal–Wallis test, and features datasets were constructed according to feature selection. Three major machine learning-based classification algorithms classified the sarcopenia group in each dataset, and the performance of classification models was compared.Results: As a result of comparing the classification model performance for sarcopenia prediction, the k-nearest neighborhood algorithm (kNN) classification model using 40 major features of TUG and 6mWT showed the best performance at 88%.Conclusion: This study can be used as a basic research for the development of self-monitoring technology for sarcopenia.Keywords: sarcopenia, physical activity, machine learning, inertial measurement unitKo JBKim KBShin YSHan HHan SKJung DYHong JSDove Medical Pressarticlesarcopeniaphysical activitymachine learninginertial measurement unitGeriatricsRC952-954.6ENClinical Interventions in Aging, Vol Volume 16, Pp 1723-1733 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	sarcopenia physical activity machine learning inertial measurement unit Geriatrics RC952-954.6
spellingShingle	sarcopenia physical activity machine learning inertial measurement unit Geriatrics RC952-954.6 Ko JB Kim KB Shin YS Han H Han SK Jung DY Hong JS Predicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers
description	Jeong Bae Ko,1,&ast; Kwang Bok Kim,1,&ast; Young Sub Shin,1 Hun Han,1 Sang Kuy Han,2 Duk Young Jung,3 Jae Soo Hong1 1Digital Health Care R&D Department, Korea Institute of Industrial Technology, Cheonan, Chuncheongnam-do, South Korea; 2Robotics R&D Department, Korea Institute of Industrial Technology, Ansan, Gyeonggi-do, South Korea; 3Seongnam Senior Experience Complex, Eulji University, Seongnam, Gyeonggi-do, South Korea&ast;These authors contributed equally to this workCorrespondence: Jae Soo HongDigital Health Care R&D Department, Korea Institute of Industrial Technology, Cheonan, Chuncheongnam-do, South KoreaTel +82 41-589-8412Fax +82 41-589-8413Email jshong94@kitech.re.krDuk Young JungSeongnam Senior Experience Complex, Eulji University, Seongnam, Gyeonggi-do, South KoreaTel +82 41-589-8412Email dyjung@eulji.ac.krPurpose: Sarcopenia is a symptom in which muscle mass decreases due to decreasing in the number of muscle fibers and muscle cross-sectional area as aging. This study aimed to develop a machine learning classification model for predicting sarcopenia through a inertial measurement unit (IMU)-based physical performance measurement data of female elderly.Patients and Methods: Seventy-eight female subjects from an elderly population (aged: 78.8± 5.7 years) volunteered to participate in this study. To evaluate the physical performance of the elderly, the experiment conducted timed-up-and-go test (TUG) and 6-minute walk test (6mWT) with worn a single IMU. Based on literature review, 132 features were extracted from collected data. Feature selection was performed through the Kruskal–Wallis test, and features datasets were constructed according to feature selection. Three major machine learning-based classification algorithms classified the sarcopenia group in each dataset, and the performance of classification models was compared.Results: As a result of comparing the classification model performance for sarcopenia prediction, the k-nearest neighborhood algorithm (kNN) classification model using 40 major features of TUG and 6mWT showed the best performance at 88%.Conclusion: This study can be used as a basic research for the development of self-monitoring technology for sarcopenia.Keywords: sarcopenia, physical activity, machine learning, inertial measurement unit
format	article
author	Ko JB Kim KB Shin YS Han H Han SK Jung DY Hong JS
author_facet	Ko JB Kim KB Shin YS Han H Han SK Jung DY Hong JS
author_sort	Ko JB
title	Predicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers
title_short	Predicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers
title_full	Predicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers
title_fullStr	Predicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers
title_full_unstemmed	Predicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers
title_sort	predicting sarcopenia of female elderly from physical activity performance measurement using machine learning classifiers
publisher	Dove Medical Press
publishDate	2021
url	https://doaj.org/article/e3372073a3994de7b9f19a98c620a453
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Predicting Sarcopenia of Female Elderly from Physical Activity Performance Measurement Using Machine Learning Classifiers

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