Thu, 21 Aug 2008 11:06:37 BST CiteULike: hyoons Philipose http://www.citeulike.org/user/hyoon/author/Philipose CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/hyoon/article/1168292 <i>Pervasive Computing, IEEE, Vol. 3, No. 4. (2004), pp. 50-57.</i><br /><br />A key aspect of pervasive computing is using computers and sensor networks to effectively and unobtrusively infer users' behavior in their environment. This includes inferring which activity users are performing, how they're performing it, and its current stage. Recognizing and recording activities of daily living is a significant problem in elder care. A new paradigm for ADL inferencing leverages radio-frequency-identification technology, data mining, and a probabilistic inference engine to recognize ADLs, based on the objects people use. We propose an approach that addresses these challenges and shows promise in automating some types of ADL monitoring. Our key observation is that the sequence of objects a person uses while performing an ADL robustly characterizes both the ADL's identity and the quality of its execution. So, we have developed Proactive Activity Toolkit (PROACT). Inferring activities from interactions with objects M Philipose KP Fishkin M Perkowitz DJ Patterson D Fox H Kautz D Hahnel Pervasive Computing, IEEE, Vol. 3, No. 4. (2004), pp. 50-57. 2007-03-17T06:19:17-00:00 2004 Pervasive Computing, IEEE 3 4 50 57 activity inference interaction object