Inferring activities from interactions with objects

@article{citeulike:1168292, abstract = {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).}, author = {Philipose, M. and Fishkin, K. P. and Perkowitz, M. and Patterson, D. J. and Fox, D. and Kautz, H. and Hahnel, D. }, citeulike-article-id = {1168292}, journal = {Pervasive Computing, IEEE}, keywords = {activity, inference, interaction, object}, number = {4}, pages = {50--57}, posted-at = {2007-03-17 06:19:50}, priority = {0}, title = {Inferring activities from interactions with objects}, url = {http://ieeexplore.ieee.org/xpls/abs\_all.jsp?arnumber=1369161}, volume = {3}, year = {2004} }

TY - JOUR ID - citeulike:1168292 L3 - citeulike-article-id:1168292 TI - Inferring activities from interactions with objects JF - Pervasive Computing, IEEE VL - 3 IS - 4 SP - 50 EP - 57 N2 - 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). KW - activity KW - inference KW - interaction KW - object AU - Philipose, M AU - Fishkin, KP AU - Perkowitz, M AU - Patterson, DJ AU - Fox, D AU - Kautz, H AU - Hahnel, D PY - 2004/// UR - http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1369161 ER -