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Constraint-Based Activity Recognition with Uncertainty
Örebro University, School of Science and Technology.
2011 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In the context of intelligent environments with the ability to provide support

within our homes and in the workplace, the activity recognition process plays

a critical role. Activity recognition can be applied to many real-life, humancentric

problems such as elder care and health care. This thesis focuses on the

recognizing high level human activity through a model driven approach to activity

recognition, whereby a constraint-based domain description is used to

correlate sensor readings to human activities. An important quality of sensor

readings is that they are often uncertain or imprecise. Hence, in order to have

a more realistic model, uncertainty in sensor data and flexibility and expressiveness

should be considered in the model. These needs naturally arise in real

world applications where considering uncertainty is crucial.

In this thesis, a previously developed approach to activity recognition based

on temporal constraint propagation is extended to accommodate uncertainty in

the sensor readings and temporal relations between activities. The result of this

extension is an activity recognition system in which each hypothesis deduced

by the system is also weighted with a possibility degree.

We validate our solutions to activity recognition with uncertainty both theoretically

and experimentally, describing some explanatory examples.


Place, publisher, year, edition, pages
2011. , 75 p.
National Category
Computer and Information Science
URN: urn:nbn:se:oru:diva-20408ISRN: ORU-NAT/DAT-AS-2011/0003--SEOAI: diva2:458227
Subject / course
Computer Engineering
Available from: 2011-11-22 Created: 2011-11-22 Last updated: 2011-11-22Bibliographically approved

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