Inferring Activities from Interactions with Objects
Citations Over TimeTop 1% of 2004 papers
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).
Related Papers
- Defining Smart Space in the Context of Ubiquitous Computing(2009)
- → Artificial-Intelligence-Assisted Activities of Daily Living Recognition for Elderly in Smart Home(2022)17 cited
- → Proactively Guiding Patients Through ADL via Knowledge-Based and Context-Driven Activity Recognition(2019)1 cited
- → Ubiquitous/pervasive intelligence: Visions and challenges(2009)
- → Keynote II: Ubiquitous/pervasive intelligence: Visions and challenges(2008)