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Real-Time Activity Classification Using Ambient and Wearable Sensors

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5 Author(s)
Atallah, L. ; Dept. of Comput., Imperial Coll. London, London, UK ; Lo, B. ; Ali, R. ; King, R.
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New approaches to chronic disease management within a home or community setting offer patients the prospect of more individually focused care and improved quality of life. This paper investigates the use of a light-weight ear worn activity recognition device combined with wireless ambient sensors for identifying common activities of daily living. A two-stage Bayesian classifier that uses information from both types of sensors is presented. Detailed experimental validation is provided for datasets collected in a laboratory setting as well as in a home environment. Issues concerning the effective use of the relatively limited discriminative power of the ambient sensors are discussed. The proposed framework bodes well for a multi-dwelling environment, and offers a pervasive sensing environment for both patients and care-takers.

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Information Technology in Biomedicine, IEEE Transactions on  (Volume:13 ,  Issue: 6 )