CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi-Inhabitant Smart Homes
Links to Fileshttps://ieeexplore.ieee.org/document/7536552/
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Type of Work10 PAGES
conference papers and proceedings preprints
Citation of Original PublicationM. A. U. Alam, N. Roy, A. Misra and J. Taylor, "CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi-inhabitant Smart Homes," 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara, 2016, pp. 539-548
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We propose CACE (Constraints And Correlations mining Engine) which investigates the challenges of improving the recognition of complex daily activities in multi-inhabitant smart homes, by better exploiting the spatiotemporal relationships across the activities of different individuals. We first propose and develop a loosely-coupled Hierarchical Dynamic Bayesian Network (HDBN), which both (a) captures the hierarchical inference of complex (macro-activity) contexts from lower-layer microactivity context (postural and improved oral gestural context), and (b) embeds the various types of behavioral correlations and constraints (at both micro-and macro-activity contexts) across the individuals. While this model is rich in terms of accuracy, it is computationally prohibitive, due to the explosive increase in the number of jointly-defined states. To tackle this challenge, we employ data mining to learn behaviorally-driven context correlations in the form of association rules, we then use such rules to prune the state space dramatically. To evaluate our framework, we build a customized smart home system and collected naturalistic multi-inhabitant smart home activities data. The system performance is illustrated with results from real-time system deployment experiences in a smart home environment reveals a radical (max 16 fold) reduction in the computational overhead compared to traditional hybrid classification approaches, as well as an improved activity recognition accuracy of max 95%.