The nature of statistical learning theory
The nature of statistical learning theory
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using TouchPad Pressure to Detect Negative Affect
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Toward a decision-theoretic framework for affect recognition and user assistance
International Journal of Human-Computer Studies - Human-computer interaction research in the managemant information systems discipline
Automatic prediction of frustration
International Journal of Human-Computer Studies
Automated stress detection using keystroke and linguistic features: An exploratory study
International Journal of Human-Computer Studies
A pressure-sensing mouse button for multilevel click and drag
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction
USAB'11 Proceedings of the 7th conference on Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society: information Quality in e-Health
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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.