Infrastructure-Assisted Smartphone-based ADL Recognition in Multi-Inhabitant Smart Environments
Conference Proceeding Article
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.
2013 IEEE International Conference on Pervasive Computing and Communications (PerCom)
City or Country
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 (PerCom). 38-46. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1658