Abstract
Accurately classifying physical activity is a big undertaking in a variety of industries, from healthcare to sports analytics. Physical activity plays a critical role in maintaining health and well-being. This review article offers a thorough overview of the many approaches and procedures used to categorize physical activities. We look at how the classification of physical activity has changed over time, from conventional approaches to the most recent developments in machine learning and sensor technologies. The study discusses several algorithms and methods used in physical activity classification, including adaptive boosting, random forest, KNN, and artificial neural network.
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Naik, R., Shrivastava, H.V., Kadam, M., Jain, I., Singh, K. (2024). Physical Activity Detection and Tracking—Review. In: Joshi, A., Mahmud, M., Ragel, R.G., Karthik, S. (eds) ICT: Innovation and Computing. ICTCS 2023. Lecture Notes in Networks and Systems, vol 879. Springer, Singapore. https://doi.org/10.1007/978-981-99-9486-1_19
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DOI: https://doi.org/10.1007/978-981-99-9486-1_19
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