Inferring Activities from Interactions with Objects

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	Similar Articles Articles by Matthai Philipose Articles by Kenneth P. Fishkin Articles by Mike Perkowitz Articles by Donald J. Patterson Articles by Dieter Fox Articles by Henry Kautz Articles by Dirk Hahnel

October-December 2004 (vol. 3 no. 4)

pp. 50-57

	ASCII Text	x
	Matthai Philipose, Kenneth P. Fishkin, Mike Perkowitz, Donald J. Patterson, Dieter Fox, Henry Kautz, Dirk Hahnel, "Inferring Activities from Interactions with Objects," IEEE Pervasive Computing, vol. 3, no. 4, pp. 50-57, October-December, 2004.

	BibTex	x
	@article{ 10.1109/MPRV.2004.7, author = {Matthai Philipose and Kenneth P. Fishkin and Mike Perkowitz and Donald J. Patterson and Dieter Fox and Henry Kautz and Dirk Hahnel}, title = {Inferring Activities from Interactions with Objects}, journal ={IEEE Pervasive Computing}, volume = {3}, number = {4}, issn = {1536-1268}, year = {2004}, pages = {50-57}, doi = {http://doi.ieeecomputersociety.org/10.1109/MPRV.2004.7}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - MGZN JO - IEEE Pervasive Computing TI - Inferring Activities from Interactions with Objects IS - 4 SN - 1536-1268 SP50 EP57 EPD - 50-57 A1 - Matthai Philipose, A1 - Kenneth P. Fishkin, A1 - Mike Perkowitz, A1 - Donald J. Patterson, A1 - Dieter Fox, A1 - Henry Kautz, A1 - Dirk Hahnel, PY - 2004 KW - context-aware computing KW - sensor networks KW - ADL monitoring KW - Proactive Activity Toolkit KW - Proact VL - 3 JA - IEEE Pervasive Computing ER -

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

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MPRV.2004.7

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.

Index Terms:

context-aware computing, sensor networks, ADL monitoring, Proactive Activity Toolkit, Proact

Citation:

Matthai Philipose, Kenneth P. Fishkin, Mike Perkowitz, Donald J. Patterson, Dieter Fox, Henry Kautz, Dirk Hahnel, "Inferring Activities from Interactions with Objects," IEEE Pervasive Computing, vol. 3, no. 4, pp. 50-57, Oct.-Dec. 2004, doi:10.1109/MPRV.2004.7

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