Title

Infrastructure-Assisted Smartphone-based ADL Recognition in Multi-Inhabitant Smart Environments

Publication Type

Conference Proceeding Article

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.

Discipline

Software Engineering

Research Areas

Software Systems

Publication

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

First Page

38

Last Page

46

ISBN

9781467345743

Identifier

10.1109/PerCom.2013.6526712

Publisher

IEEE

City or Country

San Diego

Additional URL

http://dx.doi.org/10.1109/PerCom.2013.6526712

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