Infrastructure-Assisted Smartphone-based ADL Recognition in Multi-Inhabitant Smart Environments
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 `hi...
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| Main Authors: | , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2013
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/1658 https://ink.library.smu.edu.sg/context/sis_research/article/2657/viewcontent/Infrastructure_Assisted_Smartphone_2013_afv.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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. |
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