Abstract
Activity recognition for the purposes of recognizing a user’s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables utilizing different sources of sensor data. In this paper, we propose a smartphone based Hierarchical Activity Recognition Framework which extends the Naïve Bayes approach for the processing of activity modeling and real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Naïve Bayes approach and also enables the recognition of a user’s activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%.
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Han, M., Bang, J.H., Nugent, C., McClean, S., Lee, S. (2013). HARF: A Hierarchical Activity Recognition Framework Using Smartphone Sensors. In: Urzaiz, G., Ochoa, S.F., Bravo, J., Chen, L.L., Oliveira, J. (eds) Ubiquitous Computing and Ambient Intelligence. Context-Awareness and Context-Driven Interaction. Lecture Notes in Computer Science, vol 8276. Springer, Cham. https://doi.org/10.1007/978-3-319-03176-7_21
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DOI: https://doi.org/10.1007/978-3-319-03176-7_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-03175-0
Online ISBN: 978-3-319-03176-7
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