A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents
DOI:
https://doi.org/10.31949/ijsm.v3i2.7145Abstract
The ongoing evolution of technology has had both positive and negative effects on modern society. On the positive side, it has significantly improved the ease with which various activities can be performed. However, it has also had a negative impact by reducing physical activity. This reduction in physical activity, in turn, increases the risk of chronic diseases that contribute to global mortality rates. This research aims to assess the effectiveness of machine learning in predicting the physical activity levels of adolescents. The study utilizes data from accelerometers, specifically the ActiGraph GT3X. The research methodology employs a semi-supervised machine learning approach, using the support vector machine and decision tree algorithms to make these predictions. The sample comprises 61 adolescents (males = 17, female = 44), including high school students and university students aged 18-21, from the West Java region. The results from the machine learning model using the decision tree algorithm indicated a model accuracy of 97.50% in predicting physical activity levels. In contrast, the accuracy obtained from the performance analysis using the confusion matrix for the support vector machine model was 92.5%. Based on these accuracy levels, the decision tree algorithm outperforms the support vector machine algorithm's accuracy. Further analyses involving different models are necessary to determine which algorithm offers the highest level of accuracy.
Keywords:
Accelerometer; physical activity; descision tree; SVMDownloads
References
Adawiyah, R. (2023). Eksplorasi kapasitas pengkodean amplitudo untuk model quantum machine learning. Jurnal teknik informatika dan multimedia, 3(1), 38-58.
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.
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). https://doi.org/10.3390/life12081103
Amtarina, R. (2017). Manfaat aktivitas fisik teratur terhadap perbaikan fungsi kognitif pasien dengan mild cognitive impairment. Jurnal Ilmu Kedokteran (Journal of Medical Science), 10(2), 140-147.
Ayabe, M., Kumahara, H., Morimura, K., & Tanaka, H. (2013). Epoch length and the physical activity bout analysis: an accelerometry research issue. BMC research notes, 6(1), 1-7.
Aziz, F. (2021). Klasifikasi Physical Activity Berbasis Sensor Accelorometer, Gyroscope dan Gravity Menggunakan Algoritma Multi-Class Ensemble Gradientboost. Deepublish.
Besson, H., Brage, S., Jakes, R. W., Ekelund, U., & Wareham, N. J. (2010). Estimating physical activity energy expenditure, sedentary time, and physical activity intensity by self-report in adults. The American journal of clinical nutrition, 91(1), 106-114.
Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., & Singh, P. (2021). Prediction of heart disease using a combination of machine learning and deep learning. Computational intelligence and neuroscience, 2021.
Biró, A., Szilágyi, S. M., Szilágyi, L., Martín-Martín, J., & Cuesta-Vargas, A. I. (2023). Machine learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer. Sensors, 23(7). https://doi.org/10.3390/s23073595
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
Cheng, X., Lin, S. Y., Liu, J., Liu, S., Zhang, J., Nie, P., Fuemmeler, B. F., Wang, Y., & Xue, H. (2021). Does physical activity predict obesity—a machine learning and statistical method-based analysis. International Journal of Environmental Research and Public Health, 18(8). https://doi.org/10.3390/ijerph18083966
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, 45–53. https://doi.org/10.1016/j.gaitpost.2021.06.017
Corder, K., van Sluijs, E. M., Wright, A., Whincup, P., Wareham, N. J., & Ekelund, U. (2009). Is it possible to assess free-living physical activity and energy expenditure in young people by self-report?. The American journal of clinical nutrition, 89(3), 862-870.
Dijkhuis, T. B., Blaauw, F. J., van Ittersum, M. W., Velthuijsen, H., & Aiello, M. (2018). Personalized physical activity coaching: A machine learning approach. Sensors (Switzerland), 18(2). https://doi.org/10.3390/s18020623
Ellis, K., Kerr, J., Godbole, S., Lanckriet, G., Wing, D., & Marshall, S. (2014). A random forest classifier for the prediction of energy expenditure and type of physical activity from wrist and hip accelerometers. Physiological measurement, 35(11), 2191.
Hasanudin, H., Ardiyani, V. M., & Perwiraningtyas, P. (2018). Hubungan aktivitas fisik dengan tekanan darah pada masyarakat penderita hipertensi di wilayah Tlogosuryo Kelurahan Tlogomas Kecamatan Lowokwaru Kota Malang. Nursing News: Jurnal Ilmiah Keperawatan, 3(1).
Ishikawa-Takata, K., Tabata, I., Sasaki, S., Rafamantanantsoa, H. H., Okazaki, H., Okubo, H., Tanaka, S., Yamamoto, S., Shirota, T., Uchida, K., & Murata, M. (2008). Physical activity level in healthy free-living Japanese estimated by doubly labelled water method and International Physical Activity Questionnaire. European Journal of Clinical Nutrition, 62(7), 885–891. https://doi.org/10.1038/sj.ejcn.1602805
Jacobs Jr, D. R., Ainsworth, B. E., Hartman, T. J., & Leon, A. S. (1993). A simultaneous evaluation of 10 commonly used physical activity questionnaires. Medicine and science in sports and exercise, 25(1), 81-91.
Li, K., Shi, Q., Liu, S., Xie, Y., & Liu, J. (2021). Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree. Medicine, 100(19).
Turege, J. N., Kinasih, A., & Kurniasari, M. D. (2019). Hubungan Antara Aktivitas Fisik Dengan Obesitas Di Puskesmas Tegalrejo, Kota Salatiga. Jurnal Ilmu Keperawatan Dan Kebidanan, 10(1), 256-263.
Jones, P. J., Catt, M., Davies, M. J., Edwardson, C. L., Mirkes, E. M., Khunti, K., Yates, T., & Rowlands, A. V. (2021). Feature selection for unsupervised machine learning of accelerometer data physical activity clusters – A systematic review. In Gait and Posture (Vol. 90, pp. 120–128). Elsevier B.V. https://doi.org/10.1016/j.gaitpost.2021.08.007
Mardiana, T., & Purnanto, A. W. (2017). Google form sebagai alternatif pembuatan latihan soal evaluasi. URECOL, 183-188.
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071-22080.
Osisanwo, F. Y., Akinsola, J. E. T., Awodele, O., Hinmikaiye, J. O., Olakanmi, O., & Akinjobi, J. (2017). Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT), 48(3), 128-138.
Pfeiffer, K. A., Mciver, K. L., Dowda, M., Almeida, M. J., & Pate, R. R. (2006). Validation and calibration of the Actical accelerometer in preschool children. Medicine & Science in Sports & Exercise, 38(1), 152-157.
Pramono, A., & Sulchan, M. (2014). Kontribusi makanan jajan dan aktivitas fisik terhadap kejadian obesitas pada remaja di kota Semarang. Gizi Indonesia, 37(2), 129-136.
Pratama, R. R. (2020). Analisis Model Machine learning Terhadap Pengenalan Aktifitas Manusia. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 19(2), 302–311. https://doi.org/10.30812/matrik.v19i2.688
Roihan, A., Abas Sunarya, P., & Rafika, A. S. (2019). IJCIT (Indonesian Journal on Computer and Information Technology) Pemanfaatan Machine learning dalam Berbagai Bidang: Review paper. In IJCIT (Indonesian Journal on Computer and Information Technology) (Vol. 5, Issue 1).
Romanzini, M., Petroski, E. L., Ohara, D., Dourado, A. C., & Reichert, F. F. (2014). Calibration of ActiGraph GT3X, Actical and RT3 accelerometers in adolescents. European Journal of Sport Science, 14(1), 91–99. https://doi.org/10.1080/17461391.2012.732614
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.
Sliepen, M., Lipperts, M., Tjur, M., & Mechlenburg, I. (2019). Use of accelerometer-based activity monitoring in orthopaedics: Benefits, impact and practical considerations. EFORT Open Reviews, 4(12), 678–685. https://doi.org/10.1302/2058-5241.4.180041
Strath, S. J., Kaminsky, L. A., Ainsworth, B. E., Ekelund, U., Freedson, P. S., Gary, R. A., ... & Swartz, A. M. (2013). Guide to the assessment of physical activity: clinical and research applications: a scientific statement from the American Heart Association. Circulation, 128(20), 2259-2279.
Sylvia, L. G., Bernstein, E. E., Hubbard, J. L., Keating, L., & Anderson, E. J. (2014). Practical guide to measuring physical activity. Journal of the Academy of Nutrition and Dietetics, 114(2), 199–208. https://doi.org/10.1016/j.jand.2013.09.018
Suherman, A., Sultoni, K., & Zaky, M. (2021). Physical Activity and Sedentary Behavior in University Student During Online Learning: The Effect of Covid-19 Pandemic. In Malaysian Journal of Medicine and Health Sciences (Vol. 17, Issue SUPP14).
Sulastri, S., & Rini, S. H. S. (2022). Hubungan Jenis Aplikasi Gadget Terhadap Perkembangan Anak Usia Pra Sekolah Di Kecamatan Weleri. Jurnal Surya Muda, 4(2), 118-132.
Suryani, N., Noviana, & Libri, O. (2020). Hubungan Status Gizi, Aktivitas Fisik, Konsumsi Buah dan Sayur dengan Kejadian Hipertensi di Poliklinik Penyakit Dalam RSD Idaman Kota Banjarbaru. The Indonesian Journal of Health.
Tudor-locke, C., Ainsworth, B. E., Thompson, R. W., Matthews, C. E., Ainsworth, B. E., Thompson, W. W., & Matthews, C. E. (2002). Comparison of pedometer and acceler-ometer measures of free-living physical activity. Med. Sci. Sports Exerc, 34(12), 2045–2051. https://doi.org/10.1249/01.MSS.0000039300.76400.16
Vanstrum, E. B., Choi, J. S., Bensoussan, Y., Bassett, A. M., Crowson, M. G., & Chiarelli, P. A. (2023). Machine learning Analysis of Physical Activity Data to Classify Postural Dysfunction. Laryngoscope. https://doi.org/10.1002/lary.30698
Wang, M. (2022). Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine. Advances in Multimedia, 2022. https://doi.org/10.1155/2022/4943510
Westerterp, K. R. (2009). Assessment of physical activity: a critical appraisal. European journal of applied physiology, 105, 823-828.
Wicaksono, A. (2020). Aktivitas Fisik Yang Aman Pada Masa Pandemi Covid-19. Jurnal Ilmu Keolahragaan Undiksha, 8(1), 10-15.
Willumsen, J., & Bull, F. (2020). Development of WHO guidelines on physical activity, sedentary behavior, and sleep for children less than 5 years of age. Journal of Physical Activity and Health, 17(1), 96–100. https://doi.org/10.1123/jpah.2019-0457
Zhang, S., Rowlands, A. V., Murray, P., & Hurst, T. L. (2012). Physical activity classification using the GENEA wrist-worn accelerometer. Medicine and Science in Sports and Exercise, 44(4), 742–748. https://doi.org/10.1249/MSS.0b013e31823bf95c
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). https://doi.org/10.1186/s12911-019-0890-0
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