Natural Interaction

The course describes and analyses theory and techniques for the design of artificial agents that are able to:
- perceive the user's behavioural signals, via contactless sensors (Kinect,Leap motion, video camere eye-trackers, etc) and extract their most relevant cues
- infer and predict, from such cues in a given context, user's behavioural state
- provide the appropriate feedback to the user
To such end, the course concerns: a rigorous introduction to the neurobiological and psychological models of natural interaction through gestures, postures and gaze; statistical machine learning and inference for modelling the dynamics of interaction

Upon completion of the course students will be able to:
1. Define the methodology and the most appropriate techniques for modelling interaction agents dealing with uncertainty
2. Measure and analyse behavioural signals, and extract their most informative cues
3. Design and implement simple interaction agents to be exploited in different applications such as smart living ambients, behavioural biometrics, entertainment, video surveillance.
These objectives are measured via a combination of three components: the project realisation, the project technical report and the oral discussion. The final grade is formed by assessing the software developed, the project report, and then using the oral discussion for fine tuning.