Abstract
Human activity recognition from ubiquitous sensor data is an important but challenging classification problem for applications such as assisted living, energy management, and security monitoring of smart homes. In this paper, we present a soft probabilistic classification model for human activity recognition from multi-modal sensors in a smart home environment. The model employs a softmax multi-task learning approach to fit a joint model for all the rooms in the smart home, taking into account the diverse types of sensors available in different rooms. The model also learns the transitional dependencies between activities to improve its prediction accuracy. Experimental results on a real-world dataset showed that the proposed approach outperforms several baseline methods, including k-nearest neighbors, conditional random field, and standard multinomial logistic regression.
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This research is supported in part by the U.S. National Science Foundation through grant NSF III-1615612. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
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Liu, X., Tan, PN., Liu, L. (2018). STARS: Soft Multi-Task Learning for Activity Recognition from Multi-Modal Sensor Data. In: Phung, D., Tseng, V., Webb, G., Ho, B., Ganji, M., Rashidi, L. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2018. Lecture Notes in Computer Science(), vol 10938. Springer, Cham. https://doi.org/10.1007/978-3-319-93037-4_45
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