Inferring Activities from Interactions with Objects

TLDR

Pervasive computing seeks to unobtrusively infer users’ behavior, including daily activities, which is crucial for elder care, and a new paradigm uses RFID, data mining, and probabilistic inference to recognize ADLs from object usage. The authors propose the Proactive Activity Toolkit (PROACT) to automate monitoring of certain daily activities. PROACT employs RFID, data mining, and a probabilistic inference engine to recognize ADLs from the sequence of objects used. The study finds that object‑usage sequences reliably identify ADLs and their execution quality, indicating that PROACT can effectively automate monitoring of these activities.

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).