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Activity recognition using temporal evidence theory
Date Issued
2010
Date Available
2011-05-24T11:15:56Z
Abstract
The ability to identify the behavior of people in a home is at the core of Smart Home functionality. Such environments are equipped with sensors that unobtrusively capture information about the occupants. Reasoning mechanisms transform the technical, frequently noisy data of sensors into meaningful interpretations of occupant activities. Time is a natural human way to reason about activities. People's activities in the home often have an identifiable routine; activities place at distinct times throughout the day and last for predicable lengths of time. However, the inclusion of temporal information is still limited in the domain of activity recognition. Evidence theory is gaining increasing interest in the field of activity recognition, and is suited to the incorporation of time related domain knowledge into the reasoning process. In this paper, an evidential reasoning framework that incorporates temporal knowledge is presented. We evaluate the effectiveness of the framework using a third party published smart home dataset. An improvement in activity recognition of 70% is achieved when time patterns and activity durations are included in activity recognition. We also compare our approach with Naïve Bayes classifier and J48 Decision Tree, with temporal evidence theory achieving higher accuracies than both classifiers.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
IOS Press
Journal
Journal of Ambient Intelligence and Smart Environments
Volume
2
Issue
3
Start Page
253
End Page
269
Copyright (Published Version)
2010 IOS Press and the authors.
Subject – LCSH
Case-based reasoning
Human activity recognition
Dempster-Shafer theory
Home automation
Web versions
Language
English
Status of Item
Peer reviewed
ISSN
1876-1364 (Print)
1876-1372 (Online)
This item is made available under a Creative Commons License
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