Inferring Activities from Interactions with Objects

  • Authors:
  • Matthai Philipose;Kenneth P. Fishkin;Mike Perkowitz;Donald J. Patterson;Dieter Fox;Henry Kautz;Dirk Hahnel

  • Affiliations:
  • Intel Research Seattle;Intel Research Seattle;Intel Research Seattle;University of Washington;University of Washington;University of Washington;University of Freiburg

  • Venue:
  • IEEE Pervasive Computing
  • Year:
  • 2004

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Abstract

Recognizing and recording activities of daily living is a significant problem in elder care. Ubicomp systems targeting ADL recognition have been limited in the number of ADLs they recognize, the detail they recognize, and their robustness. A new paradigm for ADL inferencing focuses on the objects people use during their day. To do this, it leverages three techniques: radio-frequency-identification technology to sense objects being touched, data mining to partially automate model creation, and a probabilistic inference engine. To test the concept, 14 people performed ADLs in a real house containing 108 tagged objects.