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Activity Recognition in Smart Homes

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Abstract

In recent years, activity recognition in smart homes is an active research area. It is an important problem of Human Computer Interaction (HCI), and has many applications in HCI, such as assistive living and healthcare. Recognition of users’ common behaviors allows an environment to provide personalized service. Unlike activity recognition in computer vision which uses cameras, it studies activities by embedded sensors in smart homes. In this paper, we propose a method to extract latent features from sensor data by Beta Process Hidden Markov Model (BP-HMM). The contributions of our method are twofold: 1, we extend BP-HMM by dependent Beta process, and integrate state constraints of sensors into the sampling process of BP-HMM. 2, we extract latent features automatically by our dependent BP-HMM, and train a structural support vector machine (SVM) by these features in a supervised way for activity recognition. To evaluate the proposed method, we performed experiments on the real-world smart home datasets. Our results suggest that extracting latent features from sensor data leads to good performance for activity recognition.

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Acknowledgments

This work was supported by the following grants: Guangdong Public Welfare and Ability Construction Project 2015B010103003, and Guangdong Collaborative Innovation and Platform Construction Project 2016A040403048.

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Authors and Affiliations

  1. School of Mathematics and Statistics, Guangdong University of Finance & Economics, Guangzhou, 510320, China

    Lu Lu

  2. School of Engineering, Sun Yat-sen University, Guangzhou, 510006, China

    Cai Qing-ling & Zhan Yi-Ju

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  1. Lu Lu
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  2. Cai Qing-ling
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Correspondence to Cai Qing-ling.

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Lu, L., Qing-ling, C. & Yi-Ju, Z. Activity Recognition in Smart Homes. Multimed Tools Appl 76, 24203–24220 (2017). https://doi.org/10.1007/s11042-016-4197-1

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