Tracking and behavior augmented activity recognition for multiple inhabitants
Publication Type
Journal Article
Publication Date
1-2021
Abstract
We develop CACE (Constraints And Correlations mining Engine), a framework that significantly improves the recognition accuracy of complex daily activities in multi-inhabitant smarthomes. CACE views the implicit relationships between the activities of multiple people as an asset, and exploits such constraints and correlations in a hierarchical fashion, taking advantage of both person-specific sensor data (generated by wearable devices) and person-independent ambient sensor data (generated by ambient sensors). To effectively utilize such couplings, CACE first uses a multi-target particle filtering approach over ambient sensors captured movement data, to identify the number of distinct users and infer individual-specific movement trajectories. We then utilize a Hierarchical Dynamic Bayesian Network (HDBN)-based model for activity recognition. This model utilizes the inter-and-intra individual correlations and constraints, at both micro-activity and macro-activity levels, to recognize individual activities accurately. These constraints are learnt automatically using data-mining techniques, and help to dramatically reduce the computational complexity of HDBN-based inferencing. Empirical studies using a real-world testbed of five multi-inhabitant smarthomes shows that CACE is able to achieve an activity recognition accuracy of approximate to 95%, with a 16-fold reduction in computational overhead compared to traditional hybrid classification approaches.
Keywords
Multiple inhabitants, multi-modal sensing, scalable activity recognition, tracking
Discipline
Numerical Analysis and Scientific Computing | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
IEEE Transactions on Mobile Computing
Volume
20
Issue
1
First Page
247
Last Page
262
ISSN
1536-1233
Identifier
10.1109/TMC.2019.2936382
Publisher
Institute of Electrical and Electronics Engineers
Citation
UL ALAM, Mohammad Arif; ROY, Nirmalya; and MISRA, Archan.
Tracking and behavior augmented activity recognition for multiple inhabitants. (2021). IEEE Transactions on Mobile Computing. 20, (1), 247-262.
Available at: https://ink.library.smu.edu.sg/sis_research/6907
Additional URL
https://doi.org/10.1109/TMC.2019.2936382
