Activity recognition using temporal evidence theory

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Title: Activity recognition using temporal evidence theory
Authors: McKeever, Susan
Ye, Juan
Coyle, Lorcan
Bleakley, Chris J.
Dobson, Simon
Permanent link: http://hdl.handle.net/10197/2947
Date: 2010
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.
Funding Details: Science Foundation Ireland
Type of material: Journal Article
Publisher: IOS Press
Copyright (published version): 2010 IOS Press and the authors.
Keywords: Smart home datasetTimeContext reasoningActivity recognitionEvidence theoryDempster-shafer theoryTemporal
Subject LCSH: Case-based reasoning
Human activity recognition
Dempster-Shafer theory
Home automation
DOI: 10.3233/AIS-2010-0071
Language: en
Status of Item: Peer reviewed
Appears in Collections:CLARITY Research Collection
Computer Science Research Collection

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