[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123333-en":3,"doc-seo-123333-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123333,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Classifying Physical Activity Levels in Early Childhood Using Actigraph and Machine Learning Method","Actigraph accelerometry enables time-based cut-point classification of physical activity, yet accurate labeling for early childhood remains challenging. This study classifies physical activity in 4–5-year-old children using World Health Organization guidance with Actigraph GT3X data and machine learning. Fifty-two young children in West Java were monitored for seven days. Decision tree and support vector machine models were implemented in RapidMiner, yielding 96.00% accuracy with decision trees and 84.67% with SVM, indicating decision trees better support early-childhood activity categorization and motivates further development.","INDONESIAN JOURNAL OFSPORT MANAGEMENT  \n# Classifying Physical Activity Levels in Early ChildhoodUsing Actigraph and Machine Learning Method\n\nSyifa Wandani¹A-E*,Adang Suherman²AE,Jajat³A-E,Kuston Sultoni⁴,Yati Ruhayati⁵D,Imas Damayanti⁶D,Nur Indri Rahayu7D  \n1-7Fakultas Pendidikan Olahraga dan Kesehatan,Universitas Pendidikan Indonesia,Bandung,Indonesia  \n## ABSTRACT\n\nCorresponding author:  \n*Syifa Wandani,Fakultas PendidikanOlahraga dan Kesehatan,UniversitasIndonesiaE-mail:wandanisyifa29@upi.edu  \nActigraph is a widely used accelerometer for classifying physical activity levels inchildren,adolescents,adults,and older people.The dassification of physical activitylevels on Actigraph is determined through time calculations using cut-pointformulas.The study aims to classify physical activity in young children according tothe World Health Organization(WHO)guidelines using accelerometer data andmachine learning methods.The study involved 52 young children(26 girls and 26boys)aged 4 to 5 years in West Java,with an average age of 4.58 years.Theseearly childhood physical activity and sedentary behaviours were simultaneouslyrecorded using the Actigraph GT3X accelerometer for seven days.The data fromthe Actigraph were analyzed using two algorithm models:the decision tree andsupport vector machine,with the Rapidminer application.The results from thedecision tree model show a dlassification accuracy of 96.00%in categorizingphysical activities in young children.On the other hand,the support vector machinemodel achieved an accuracy of 84.67%in dassifying physical activities in youngchildren.The decision tree outperforms the support vector machine in accuratelydassifying physical activities in early childhood.This research highlights thepotential benefits of machine learning in sports and physical activity sciences,indicating the need for further development.  \nArticle History:  \nReceived:Septemberi5,2023  \nAccepted after revision:October 10,2023  \nFirst Published Online:October 30,2023  \nAuthors'contribution:  \nA)Conception and design of the study;  \nB)Acquisition of data;  \nC)Analysis and interpretation of data;  \nD)Manuscript preparation;  \nE)Obtaining funding.  \nCite this article:  \nWandani,S.,Suherman,A.,Jajat,Sultoni,K.,Ruhayati,Y.,Damayanti,I.,Rahayu,N.I.(2023).Classifying Physical Activity Levels inEarly Childhood Using Actigraph and MachineLearning Method.Indonesian Journal of SportManagement,230-241.https://doi.org/10.31949/ijsm.v3i1.7173  \nKeywords:decision tree;physical activity;PA level;support vector machine  \n## INTRODUCTION\n\nEarly childhood refers to the age group from 0 to 9 years,and it's a critical periodwhen children undergo significant physical,cognitive,and emotional development.This period is often called the \"golden age,\"where 80%of their brain is activelydeveloping.This phase is marked by rapid changes in physical,cognitive,emotional,moral,religious,and language development (Leonardo &Komaini,2021).The pace ofdevelopment varies among individuals and is influenced by genetics,physical health,nutrition,and the environment (Lin et al.,2021).Early childhood is a unique phase inlife that should not be overlooked.It's a crucial time to stimulate individualdevelopment (Talango,2020).This stimulation aims to make children active,healthy,and intelligent in their early years.Research indicates that children active in sportsduring early childhood tend to remain physically active in adulthood(Febrianta,2016).The habits formed during early childhood often persist into later stages of life,whichmakes this period even more important.  \nOver the last two decades,there has been progressing in global child birth ratesand the survival of early childhood (Lawn et al.,2014).However,despite theseadvancements,many early childhooders still suffer from illnesses and,in some cases,even lose their lives.These tragic outcomes are often caused by factors such aspremature birth complications,pneumonia,birth asphyxia,diarrhoea,and malaria(Vakili e","cbCaihiIGQjhb7cq","https://ap.wps.com/l/cbCaihiIGQjhb7cq","pdf",4398347,1,12,"English","en",105,"# ABSTRACT\n# INTRODUCTION","[{\"question\":\"What is the main goal of the study on early childhood activity classification?\",\"answer\":\"To classify physical activity levels in young children (4–5 years) using accelerometer data and machine learning methods based on WHO guidelines.\"},{\"question\":\"How was physical activity data collected in the study?\",\"answer\":\"Physical activity and sedentary behaviors were recorded simultaneously for seven days using the Actigraph GT3X accelerometer.\"},{\"question\":\"Which machine learning model performed better for classifying early childhood physical activity?\",\"answer\":\"The decision tree model achieved 96.00% accuracy, outperforming the support vector machine model with 84.67% accuracy.\"}]","Classifying Physical Activity Levels in Early Childhood Using Actigraph and Machine Learning Method | 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is the main goal of the study on early childhood activity classification?","Question",{"text":75,"@type":76},"To classify physical activity levels in young children (4–5 years) using accelerometer data and machine learning methods based on WHO guidelines.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was physical activity data collected in the study?",{"text":80,"@type":76},"Physical activity and sedentary behaviors were recorded simultaneously for seven days using the Actigraph GT3X accelerometer.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed better for classifying early childhood physical activity?",{"text":84,"@type":76},"The decision tree model achieved 96.00% accuracy, outperforming the support vector machine model with 84.67% 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