Abstract
Human activity is always a reflection of its external environmental conditions. If a group of people is in some emergency, then their activities and behaviour will be different as compared to normal conditions. To detect an emergency, Human Activity Recognition (HAR) can play an important role. Human activities such as shouting, running here and there, crying, searching for an exit door can be taken into consideration as an emergency indicator. By detecting the emergency and its degree, the Emergency Management System (EMS) can manage the situation efficiently. In this work, we use machine learning algorithms such as Random Forest (RF), IBK, Bagging, J48 and MLP on WISDM Smartphone and Smartwatch Activity and Biometric Dataset for human activity recognition and RF is found to be the best algorithm with classification accuracy 87.1977% among all other considered techniques.
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Nanda, S., Panigrahi, C.R., Pati, B., Mishra, A. (2021). A Novel Approach to Detect Emergency Using Machine Learning. In: Panigrahi, C.R., Pati, B., Mohapatra, P., Buyya, R., Li, KC. (eds) Progress in Advanced Computing and Intelligent Engineering. Advances in Intelligent Systems and Computing, vol 1199. Springer, Singapore. https://doi.org/10.1007/978-981-15-6353-9_17
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DOI: https://doi.org/10.1007/978-981-15-6353-9_17
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