Real-time activity classification using ambient and wearable sensors.
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.
Atallah
Louis
L
Department of Computing, Imperial College, London SW7 2AZ, UK. latallah@doc.ic.ac.uk
Lo
Benny
B
Ali
Raza
R
King
Rachel
R
Yang
Guang-Zhong
GZ
D000203:Activities of Daily Living; D001499:Bayes Theorem; D002908:Chronic Disease / Q000628:therapy*; D003020:Clothing; D019468:Disease Management; D004431:Ear, External; D006801:Humans; D018670:Monitoring, Ambulatory / Q000295:instrumentation* / Q000379:methods*; D016011:Normal Distribution; D013686:Telemetry / Q000295:instrumentation*