Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
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.
2011. , 75 p.