Classifying Physical Activity Levels in Early Childhood Using Actigraph and Machine Learning Method

    Syifa Wandani, - (2023) Classifying Physical Activity Levels in Early Childhood Using Actigraph and Machine Learning Method. S1 thesis, Universitas Pendidikan Indonesia.

    Abstract

    Actigraph is a widely used accelerometer for classifying physical activity levels in
    children, adolescents, adults, and older people. The classification of physical activity
    levels on Actigraph is determined through time calculations using cut-point
    formulas. The study aims to classify physical activity in young children according to
    the World Health Organization (WHO) guidelines using accelerometer data and
    machine learning methods. The study involved 52 young children (26 girls and 26
    boys) aged 4 to 5 years in West Java, with an average age of 4.58 years. These
    early childhood physical activity and sedentary behaviours were simultaneously
    recorded using the Actigraph GT3X accelerometer for seven days. The data from
    the Actigraph were analyzed using two algorithm models: the decision tree and
    support vector machine, with the Rapidminer application. The results from the
    decision tree model show a classification accuracy of 96.00% in categorizing
    physical activities in young children. On the other hand, the support vector machine
    model achieved an accuracy of 84.67% in classifying physical activities in young
    children. The decision tree outperforms the support vector machine in accurately
    classifying physical activities in early childhood. This research highlights the
    potential benefits of machine learning in sports and physical activity sciences,
    indicating the need for further development.

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    Official URL: https://www.ejournal.unma.ac.id/index.php/ijsm/art...
    Item Type: Thesis (S1)
    Additional Information: https://scholar.google.com/citations?view_op=list_works&hl=en&hl=en&user=ln0lwwoAAAAJ ID Sinta Dosen Pembimbing : Adang Suherman : 5989871 Jajat : 6002301
    Uncontrolled Keywords: decision tree; physical activity; PA level; support vector machine
    Subjects: G Geography. Anthropology. Recreation > GV Recreation Leisure
    L Education > L Education (General)
    L Education > LB Theory and practice of education
    Divisions: Fakultas Pendidikan Olahraga dan Kesehatan > Jurusan Pendidikan Kesehatan dan Rekreasi > Program Studi IKOR
    Depositing User: Syifa Wandani
    Date Deposited: 12 Feb 2024 06:50
    Last Modified: 12 Feb 2024 06:50
    URI: http://repository.upi.edu/id/eprint/115070

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