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Infrastructure-assisted smartphone-based ADL recognition in multi-inhabitant smart environments

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3 Author(s)
Nirmalya Roy ; School of Electrical Engineering and Computer Science, Washington State University ; Archan Misra ; Diane Cook

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.

Published in:

Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on

Date of Conference:

18-22 March 2013