Generic Machine Learning algorithms for Real-time Human-Computer Interaction

The goal is to build a generic classifier for gesture interaction applications. This kind of applications use directed, conscious intentional interactions that are clearly defined in time and space, for example gestures for selection and manipulation. The classifier needs to be generic and can be applied to different types of sensors and different gestures.The sensors which will be discussed are inertial measurement units (a combination of accelerometers, gyroscopes and magnetometers) and touch screens. In a second phase, 3D cameras will also be studied. An important criterion for the classifier is accuracy. This makes sure the user is correctly understood and a smooth interaction is possible. Moreover, the user need to have the feeling the application reacts immediately, so the classification needs to be real-time.

Machine learning techniqueswill be used to build this generic model. These techniques are not yet widely used in the human-computer interaction domain. However, machine learning techniques are concerned with the design and development of algorithms which learn to recognize patterns, and so these techniques are suitable for the problem of sensor data recognition. The research will focus on Support Vector Machines, a classification technique which analysesdata and recognizes patterns by transforming the data to a feature space and by searching the separating plane with maximal margin.

To achieve these goals, the following research topics are considered. First,it needs to be investigated whether the generation and selection of features from the sensor data, the first step in the design of a classifier, can be generalized. Features should no longer be selected for each sensor type by an expert. Then it is examined how the classifier can deal with transform-invariant features. These are gestures for which size, position and angle doesnt matter. Thirdly, it is investigated how to automate the tuning of the parameters for the classifier (for example the regularization constant or the kernel function). Finally, it is investigated if the sensor can be continuously monitored without knowing the start and end signal, for example by using a sliding window.