Issue No. 04 - October-December (2004 vol. 3)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MPRV.2004.7
Matthai Philipose , Intel Research Seattle
Kenneth P. Fishkin , Intel Research Seattle
Mike Perkowitz , Intel Research Seattle
Donald J. Patterson , University of Washington
Dieter Fox , University of Washington
Henry Kautz , University of Washington
Dirk Hahnel , University of Freiburg
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.
context-aware computing, sensor networks, ADL monitoring, Proactive Activity Toolkit, Proact
M. Perkowitz et al., "Inferring Activities from Interactions with Objects," in IEEE Pervasive Computing, vol. 3, no. , pp. 50-57, 2004.