Inferring activities from interactions with objects

• Published in: IEEE pervasive computing Vol. 3; no. 4; pp. 50 - 57
• Main Authors: Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Fox, D., Kautz, H., Hahnel, D.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2004; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• 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
• ISSN: 1536-1268; 1558-2590
• DOI: 10.1109/MPRV.2004.7

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).