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Activity recognition using temporal evidence theory

Fri, 09/10/2010 - 13:52 — lorcan

Publication Type:

Journal Article

Authors:

Susan McKeever; Juan Ye; Lorcan Coyle; Chris Bleakley; Simon Dobson

Source:

Journal of Ambient Intelligence and Smart Environments, IOS Press, Volume 2, Issue 3, p.253-269 (2010)

Keywords:

context; context awareness; pervasive computing; ubiquitous computing; dempster-shafer; uncertainty; situation

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. Peoples' activities in the home often have an identifiable routine; activities take 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.

Using Dempster-Shafer Theory of Evidence for Situation Inference

Tue, 10/20/2009 - 13:17 — lorcan

Publication Type:

Conference Paper

Authors:

Susan McKeever; Juan Ye; Lorcan Coyle; Simon Dobson

Source:

4th European Conference on Smart Sensing and Context (EuroSSC), Springer, The University of Surrey, Guildford, UK, p.149-162 (2009)

Keywords:

dempster-shafer; context awareness

Abstract:

In the domain of ubiquitous computing, the ability to identify the occurrence of situations is a core function of being 'context-aware'. Given the uncertain nature of sensor information and inference rules, reasoning techniques that cater for uncertainty hold promise for enabling the inference process. In our work, we apply the Dempster Shafer theory of evidence to infer situation occurrence with minimal use of training data. We describe a set of evidential operations for sensor mass functions using context quality and evidence accumulation for continuous situation detection. We demonstrate how our approach enables situation inference with uncertain information using a case study based on a published smart home activity data set.

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