Inferring activities from interactions with objects

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 a...

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Bibliographic Details
Published in:IEEE pervasive computing 2004-10, Vol.3 (4), p.50-57
Main Authors: Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Fox, D., Kautz, H., Hahnel, D.
Format: Article
Language:English
Subjects:
ADL monitoring
Computation
Context modeling
context-aware computing
Engines
Monitoring
Networks
Object recognition
Pervasive computing
Proact
Proactive Activity Toolkit
Radio frequency
Radiofrequency identification
Recognition
Recording
RFID tags
Robustness
sensor networks
Sensor phenomena and characterization
Sensor systems
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Summary: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).
ISSN:1536-1268
1558-2590
DOI:10.1109/MPRV.2004.7