Abstract
Existing work on sensor-based activity recognition focuses mainly on single-user activities. However, in real life, activities are often performed by multiple users involving interactions between them. In this paper, we propose Coupled Hidden Markov Models (CHMMs) to recognize multi-user activities from sensor readings in a smart home environment. We develop a multimodal sensing platform and present a theoretical framework to recognize both single-user and multi-user activities. We conduct our trace collection done in a smart home, and evaluate our framework through experimental studies. Our experimental result shows that we achieve an average accuracy of 85.46% with CHMMs.
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Wang, L., Gu, T., Tao, X., Lu, J. (2009). Sensor-Based Human Activity Recognition in a Multi-user Scenario. In: Tscheligi, M., et al. Ambient Intelligence. AmI 2009. Lecture Notes in Computer Science, vol 5859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05408-2_10
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DOI: https://doi.org/10.1007/978-3-642-05408-2_10
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