User type identification by mixing weight estimation of mixture models based on state space modeling

An approach to adaptive user interface using mixture model and state space model is proposed. Mixture model is applied to response data of many users to extract user types in a preliminary experiment. Estimated components are regarded as "user types". Online identification of the type of a new user from his/her response series is done by state space model, where the weights of the components constitute the state vector. In the state space model, the system equation defines a time smoothness of the weights and the observation equation consists of a mixture model allocated to the time-varying weights. State estimation is done by using particle filter. We propose to use the identification result of the new user to an adaptive user interface by showing an appropriate screen based on the estimated weights. Numerical simulation illustrates type identification result of new user. Real data analysis using key-typing performance with methods using both-hands, right (dominant)-hand, left (non-dominant)-hand, and one finger is also reported.