CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi-Inhabitant Smart Homes
| dc.contributor.author | Alam, Mohammad Arif Ul | |
| dc.contributor.author | Roy, Nirmalya | |
| dc.contributor.author | Misra, Archan | |
| dc.contributor.author | Taylor, Joseph | |
| dc.date.accessioned | 2018-09-04T18:15:42Z | |
| dc.date.available | 2018-09-04T18:15:42Z | |
| dc.date.issued | 2016-08-11 | |
| dc.description | © 2016 IEEE; 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS) | en_US |
| dc.description.abstract | 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%. | en_US |
| dc.description.sponsorship | This work is supported partially by the NSF Award #1344990, UMB-UMBC Research and Innovation Partnership Grant, and Constellation E2: Energy to Educate Grant. | en_US |
| dc.description.uri | https://ieeexplore.ieee.org/document/7536552/ | en_US |
| dc.format.extent | 10 PAGES | en_US |
| dc.genre | conference papers and proceedings preprints | en_US |
| dc.identifier | doi:10.13016/M28S4JS5X | |
| dc.identifier.citation | M. 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 | en_US |
| dc.identifier.uri | 10.1109/ICDCS.2016.61 | |
| dc.identifier.uri | http://hdl.handle.net/11603/11208 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Information Systems Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.rights | This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author. | |
| dc.subject | Conferences | en_US |
| dc.subject | Distributed computing | en_US |
| dc.subject | Mobile Pervasive & Sensor Computing Lab | en_US |
| dc.title | CACE: Exploiting Behavioral Interactions for Improved Activity Recognition in Multi-Inhabitant Smart Homes | en_US |
| dc.type | Text | en_US |