CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi inhabitant Smart Homes
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%
2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS)