A hybrid discriminative/generative approach for modeling human activities

  • Authors:

  • Jonathan Lester;Tanzeem Choudhury;Nicky Kern;Gaetano Borriello;Blake Hannaford

  • Affiliations:

  • Department of Electrical Engineering, University of Washington, Seattle, WA;Intel Research Seattle, Seattle, WA;Department of Computer Science, Darmstadt University of Technology, Darmstadt, Germany;Intel Research Seattle, Seattle, WA and Department of Computer Science, University of Washington, Seattle, WA;Department of Electrical Engineering, University of Washington, Seattle, WA

  • Venue:
  • IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
  • Year:
  • 2005

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Abstract

Accurate recognition and tracking of human activities is an important goal of ubiquitous computing. Recent advances in the development of multi-modal wearable sensors enable us to gather rich datasets of human activities. However, the problem of automatically identifying the most useful features for modeling such activities remains largely unsolved. In this paper we present a hybrid approach to recognizing activities, which combines boosting to discriminatively select useful features and learn an ensemble of static classifiers to recognize different activities, with hidden Markov models (HMMs) to capture the temporal regularities and smoothness of activities. We tested the activity recognition system using over 12 hours of wearable-sensor data collected by volunteers in natural unconstrained environments. The models succeeded in identifying a small set of maximally informative features, and were able identify ten different human activities with an accuracy of 95%.