Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
3-2013
Abstract
We propose a hybrid approach for recognizing complex Activities of Daily Living that lie between the two extremes of intensive use of body-worn sensors and the use of infrastructural sensors. Our approach harnesses the power of infrastructural sensors (e.g., motion sensors) to provide additional `hidden' context (e.g., room-level location) of an individual and combines this context with smartphone-based sensing of micro-level postural/locomotive states. The major novelty is our focus on multi-inhabitant environments, where we show how spatiotemporal constraints can be used to significantly improve the accuracy and computational overhead of traditional coupled-HMM based approaches. Experimental results on a smart home dataset demonstrate that this approach improves the accuracy of complex ADL classification by over 30% compared to pure smartphone-based solutions.
Keywords
Multi-modal sensing, context recognition
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
2013 IEEE International Conference on Pervasive Computing and Communications 14th PerCom: March 18-22, San Diego: Proceedings
First Page
38
Last Page
46
ISBN
9781467345743
Identifier
10.1109/PerCom.2013.6526712
Publisher
IEEE Computer Society
City or Country
Los Alamitos, CA
Citation
ROY, Nirmalya; MISRA, Archan; and COOK, Diane.
Infrastructure-Assisted Smartphone-based ADL Recognition in Multi-Inhabitant Smart Environments. (2013). 2013 IEEE International Conference on Pervasive Computing and Communications 14th PerCom: March 18-22, San Diego: Proceedings. 38-46.
Available at: https://ink.library.smu.edu.sg/sis_research/1658
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/PerCom.2013.6526712