Abstract
Human Activity Recognition (HAR) is a sensor-based observation of human action. HAR is used to monitor the health issues associated with human activities. HAR is used to monitor human health, observation of elderly people, and fitness tracking activity. HAR is an advanced application of Machine Learning (ML), and Artificial Intelligence is used to detect various human-related activities. In this study, we have described a few physical exercises, using Random Forest (RF) classifier. In that case, we have obtained an accuracy of 99%. We have also used a few feature selection algorithms such as Equilibrium Algorithm (EQ), Marine Predators algorithm (MPA), Tree Growth Algorithm (TGA), Artificial Butterfly Algorithm (ABO), and Bat Algorithm (BA) to reduce computational complexity and time. These algorithms were tested using a KU-HAR dataset, which provided 97%, 98%, 99.8%, 97%, and 97% accuracy in classification for the dataset used for testing. This study aims to understand the extent to which supervised machine learning algorithms can achieve well and how it affects the accuracy of sensor recognition. This approach will help researchers conduct more research in the future on the recognition of human exercise activities.
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Mondal, P.K., Awal, M.A., Nahid, AA. (2022). Machine-Learning Based Physical Exercise Identification with Heuristic Optimized Features Prioritization. In: Hossain, S., Hossain, M.S., Kaiser, M.S., Majumder, S.P., Ray, K. (eds) Proceedings of International Conference on Fourth Industrial Revolution and Beyond 2021 . Lecture Notes in Networks and Systems, vol 437. Springer, Singapore. https://doi.org/10.1007/978-981-19-2445-3_4
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