Real-time activity classification using ambient and wearable sensors

  • Authors:
  • Louis Atallah;Benny Lo;Raza Ali;Rachel King;Guang-Zhong Yang

  • Affiliations:
  • Department of Computing, Imperial College, London, U.K.;Department of Computing, Imperial College, London, U.K.;Department of Computing, Imperial College, London, U.K.;Department of Computing, Imperial College, London, U.K.;Centre for Pervasive Sensing, Imperial College, London, U.K.

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
  • Year:
  • 2009

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Abstract

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.