Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
FingerCloud: uncertainty and autonomy handover incapacitive sensing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A framework for robust and flexible handling of inputs with uncertainty
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
AnglePose: robust, precise capacitive touch tracking via 3d orientation estimation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Monte carlo methods for managing interactive state, action and feedback under uncertainty
Proceedings of the 24th annual ACM symposium on User interface software and technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
A user-specific machine learning approach for improving touch accuracy on mobile devices
Proceedings of the 25th annual ACM symposium on User interface software and technology
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As interaction methods beyond the static mouse and keyboard setup of the desktop era - such as touch, gesture sensing, and visual tracking - become more common, existing interaction paradigms are no longer good enough. These new modalities have high uncertainty, and conventional interfaces are not designed to reflect this. Research has shown that modelling uncertainty can improve the quality of interaction with these systems. Machine learning offers a rich set of tools to make probabilistic inferences in uncertain systems - this is the focus of my thesis work. In particular, I'm interested in making inferences at the sensor level and propagating uncertainty forward appropriately to applications. In this paper I describe a probabilistic model for touch interaction, and discuss how I intend to use the uncertainty in this model to improve typing accuracy on a soft keyboard. The model described here lays the groundwork for a rich framework for interaction in the presence of uncertainty, incorporating data from multiple sensors to make more accurate inferences about the goals of users, and allowing systems to adapt smoothly and appropriately to their context of use.