Classifying Physical Activity Levels in Early Childhood Using Actigraph and Machine Learning Method
DOI:
https://doi.org/10.31949/ijsm.v3i2.7173Keywords:
Decision Tree, Physical Activity, PA level, Support Vector MachineAbstract
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 guidelines of the World Health Organization (WHO) 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. Physical activity and sedentary behavior of these early childhood 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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Ahmadi, M. N., & Trost, S. G. (2022). Device-based measurement of physical activity in pre-schoolers: Comparison of machine learning and cut point methods. PLoS ONE, 17(4 April), 1–17. https://doi.org/10.1371/journal.pone.0266970
Ahmadi, M., O’Neil, M., Fragala-Pinkham, M., Lennon, N., & Trost, S. (2018). Machine learning algorithms for activity recognition in ambulant children and adolescents with cerebral palsy. Journal of NeuroEngineering and Rehabilitation, 15(1), 1–9.
Ahmed, A., Aziz, S., Qidwai, U., Farooq, F., Shan, J., Subramanian, M., ... & Sheikh, J. (2022). Wearable Artificial Intelligence for Assessing Physical Activity in High School Children. Sustainability, 15(1), 638.
Alsareii, S. A., Awais, M., Alamri, A. M., AlAsmari, M. Y., Irfan, M., Aslam, N., & Raza, M. (2022). Physical activity monitoring and classification using machine learning techniques. Life, 12(8), 1103.
Berlin, J. E., Storti, K. L., & Brach, J. S. (2006). Using activity monitors to measure physical activity in free-living conditions. Physical Therapy, 86(8), 1137–1145.
Beattie*, P. E., & Lewis‐Jones, M. S. (2006). A comparative study of impairment of quality of life in children with skin disease and children with other chronic childhood diseases. British Journal of Dermatology, 155(1), 145-151.
Bozkurt, F. (2022). A Comparative Study on Classifying Human Activities Using Classical Machine and Deep Learning Methods. Arabian Journal for Science and Engineering, 47(2), 1507–1521. https://doi.org/10.1007/s13369-021-06008-5
Brumana, L., Arroyo, A., Schwalbe, N. R., Lehtimaki, S., & Hipgrave, D. B. (2017). Maternal and child health services and an integrated, life-cycle approach to the prevention of non-communicable diseases. BMJ Global Health, 2(3), e000295.
Chong, J., Tjurin, P., Niemelä, M., Jämsä, T., & Farrahi, V. (2021). Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms. Gait and Posture, 89(January), 45–53. https://doi.org/10.1016/j.gaitpost.2021.06.017
Faisal Candrasyah Hasibuan, & Andri Ulus Rahayu. (2022). Identifikasi Persediaan Makanan di dalam Lemari Pendingin Berbasis Raspberry Pi dan Deep Learning. Electrician, 16(1), 94–101. https://doi.org/10.23960/elc.v16n1.2231
Febrianta, Y. (2016). Alternatif Mengembangkan Kemampuan Motorik Kasar Anak Usia Dini Dengan Aktivitas Akuatik (Berenang). Al Athfal: Jurnal Pendidikan Anak, 2(2), 85–95. http://ejournal.uin-suka.ac.id/tarbiyah/index.php/alathfal/article/view/1269
Fergus, P., Hussain, A. J., Hearty, J., Fairclough, S., Boddy, L., Mackintosh, K., Stratton, G., Ridgers, N., Al-Jumeily, D., Aljaaf, A. J., & Lunn, J. (2017). A machine learning approach to measure and monitor physical activity in children. Neurocomputing, 228, 220–230. https://doi.org/10.1016/j.neucom.2016.10.040
Goodlich, B. I., Armstrong, E. L., Horan, S. A., Baque, E., Carty, C. P., Ahmadi, M. N., & Trost, S. G. (2020). Machine learning to quantify habitual physical activity in children with cerebral palsy. Developmental Medicine and Child Neurology, 62(9), 1054–1060.
Brumana, L., Arroyo, A., Schwalbe, N. R., Lehtimaki, S., & Hipgrave, D. B. (2017). Maternal and child health services and an integrated, life-cycle approach to the prevention of non-communicable diseases. BMJ Global Health, 2(3), e000295.
Lawn, J. E., Blencowe, H., Oza, S., You, D., Lee, A. C. C., Waiswa, P., Lalli, M., Bhutta, Z., Barros, A. J. D., & Christian, P. (2014). Every Newborn: progress, priorities, and potential beyond survival. The Lancet, 384(9938), 189–205.
Lee, K., & Kwan, M. P. (2018). Physical activity classification in free-living conditions using smartphone accelerometer data and exploration of predicted results. Computers, Environment and Urban Systems, 67, 124-131.
Leonardo, A., & Komaini, A. (2021). Hubungan Aktivitas Fisik Terhadap Keterampilan Motorik. Jurnal Stamina, 4(3), 135–144. http://stamina.ppj.unp.ac.id/index.php/JST/article/view/764
Lin, Y., Yuan, J., Long, Q., Hu, J., Deng, H., Zhao, Z., Chen, J., Lu, M., & Huang, A. (2021). Patients with SARS-CoV-2 and HBV co-infection are at risk of greater liver injury. Genes and Diseases, 8(4), 484–492. https://doi.org/10.1016/j.gendis.2020.11.005
Long, X., Yin, B., & Aarts, R. M. (2009). Single-accelerometer-based daily physical activity classification. Proceedings of the 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Engineering the Future of Biomedicine, EMBC 2009, 6107–6110. https://doi.org/10.1109/IEMBS.2009.5334925
Lontoh, P. F., Suryawan, A., & Utami, S. (2021). Hubungan Lama Paparan Televisi Terhadap Perkembangan Motorik Halus Balita. Indonesian Midwifery and Health Sciences Journal, 3(1), 77–84. https://doi.org/10.20473/imhsj.v3i1.2019.77-84
Ludyanti, L. N. (2019). Perilaku Kurang Gerak (Sedentary Behaviour) Dengan Perkembangan Psikososial Anak Pra Sekolah. Care : Jurnal Ilmiah Ilmu Kesehatan, 7(2), 22. https://doi.org/10.33366/jc.v7i2.1290
Mahendra, M., Chandra Telaumbanua, R., Wanto, A., & Perdana Windarto, A. (2022). Akurasi Prediksi Ekspor Tanaman Obat, Aromatik dan Rempah-Rempah Menggunakan Machine Learning. KLIK: Kajian Ilmiah Informatika Dan Komputer, 2(6), 207–215. https://doi.org/10.30865/klik.v2i6.402
Maher, C. A., Davis, C. R., Curtis, R. G., Short, C. E., & Murphy, K. J. (2020). A physical activity and diet program delivered by artificially intelligent virtual health coach: proof-of-concept study. JMIR mHealth and uHealth, 8(7), e17558
Mannini, A., & Sabatini, A. M. (2010). Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors, 10(2), 1154–1175. https://doi.org/10.3390/s100201154
Mardini, M. T., Bai, C., Wanigatunga, A. A., Saldana, S., Casanova, R., & Manini, T. M. (2021). Age differences in estimating physical activity by wrist accelerometry using machine learning. Sensors, 21(10), 1–15. https://doi.org/10.3390/s21103352
Mesanza, A. B., Lucas, S., Zubizarreta, A., Cabanes, I., Portillo, E., & Rodriguez-Larrad, A. (2020). A machine learning approach to perform physical activity classification using a sensorized crutch tip. IEEE Access, 8, 210023-210034.
Nurcahyo, F. (2015). Kaitan Antara Obesitas Dan Aktivitas Fisik. Medikora, 1, 87–96. https://doi.org/10.21831/medikora.v0i1.4663
Rowlands, A. V. (2007). Accelerometer assessment of physical activity in children: An update. Pediatric Exercise Science, 19(3), 252–266. https://doi.org/10.1123/pes.19.3.252
Saqib, Z. A., Dai, J., Menhas, R., Mahmood, S., Karim, M., Sang, X., & Weng, Y. (2020). Physical activity is a medicine for non-communicable diseases: A survey study regarding the perception of physical activity impact on health wellbeing. Risk Management and Healthcare Policy, 13, 2949–2962.
Santos-Lozano, A., Santin-Medeiros, F., Cardon, G., Torres-Luque, G., Bailon, R., Bergmeir, C., ... & Garatachea, N. (2013). Actigraph GT3X: validation and determination of physical activity intensity cut points. International journal of sports medicine, 975-982.
Sheng, B., Moosman, O. M., Del Pozo-Cruz, B., Del Pozo-Cruz, J., Alfonso-Rosa, R. M., & Zhang, Y. (2020). A comparison of different machine learning algorithms, types and placements of activity monitors for physical activity classification. Measurement, 154, 107480.
Sulistia, T., Djamahar, R., & Rahayu, S. (2018). Hubungan kualitas tidur dan aktivitas fisik dengan hasil belajar kognitif sistem koordinasi manusia. Jurnal Penelitian Pendidikan Biologi, 2(2), 113–120. http://jurnal.um-palembang.ac.id/index.php/dikbio
Talango, S. R. (2020). Konsep Perkembangan Anak Usia Dini. Early Childhood Islamic Education Journal, 1(1), 92–105. https://doi.org/10.54045/ecie.v1i1.35
Telaumbanua, F. D., Hulu, P., Nadeak, T. Z., Lumbantong, R. R., & Dharma, A. (2019). Penggunaan Machine Learning. Jurnal Teknologi Dan Ilmu Komputer, 3(1), 57–64.
Vakili, R., Khademi, G., Vakili, S., & SAEIDI, M. (2015). Child mortality at different world regions: a comparison review.
Westerterp, K. R. (2013). Physical activity and physical activity induced energy expenditure in humans: Measurement, determinants, and effects. Frontiers in Physiology, 4 APR(April), 1–12. https://doi.org/10.3389/fphys.2013.00090
Zhou, M., Fukuoka, Y., Goldberg, K., Vittinghoff, E., & Aswani, A. (2019). Applying machine learning to predict future adherence to physical activity programs. BMC Medical Informatics and Decision Making, 19(1), 1–11. https://doi.org/10.1186/s12911-019-0890-0
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Copyright (c) 2023 Syifa Wandani, Adang Suherman, Jajat, Kuston Sultoni, Yati Ruhayati, Imas Damayanti, Nur Indri Rahayu
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