Constraint-Based Activity Recognition with Uncertainty

Abstract: 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.