Y. Qi, Carson Reynolds, Rosalind W. Picard

Abstract

We mount eight pressure sensors on a computer mouse and collect mouse pressure signals from subjects who fill out web forms containing usability bugs. This approach is based on a hypothesis that subjects tend to apply excess pressure to the mouse after encountering frustrating events. We then train a Bayes Point Machine in an attempt to classify two regions of each user’s behavior: mouse pressure where the form-filling process is proceeding smoothly, and mouse pressure following a usability bug. Different from current popular classifiers such as the Support Vector Machine, the Bayes Point Machine is a new classification technique rooted in the Bayesian theory. Trained with a new efficient Bayesian approximation algorithm, Expectation Propagation, the Bayes Point Machine achieves a person-dependent classification accuracy rate of 88%, which outperforms the Support Vector Machine in our experiments. The resulting system can be used for many applications in human-computer interaction including adaptive interface design.