A multi-touch three dimensional touch-sensitive tablet
CHI '85 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Back-of-device interaction allows creating very small touch devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
Smudge attacks on smartphone touch screens
WOOT'10 Proceedings of the 4th USENIX conference on Offensive technologies
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
100,000,000 taps: analysis and improvement of touch performance in the large
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
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
PocketTouch: through-fabric capacitive touch input
Proceedings of the 24th annual ACM symposium on User interface software and technology
Calibration games: making calibration tasks enjoyable by adding motivating game elements
Proceedings of the 24th annual ACM symposium on User interface software and technology
A First Course in Machine Learning
A First Course in Machine Learning
Towards online adaptation and personalization of key-target resizing for mobile devices
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Machine learning models for uncertain interaction
Adjunct proceedings of the 25th annual ACM symposium on User interface software and technology
The interactive join: recognizing gestures for database queries
CHI '13 Extended Abstracts on Human Factors in Computing Systems
User-specific touch models in a cross-device context
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
Sparse selection of training data for touch correction systems
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
Bayesian touch: a statistical criterion of target selection with finger touch
Proceedings of the 26th annual ACM symposium on User interface software and technology
Proceedings of the 19th international conference on Intelligent User Interfaces
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We present a flexible Machine Learning approach for learning user-specific touch input models to increase touch accuracy on mobile devices. The model is based on flexible, non-parametric Gaussian Process regression and is learned using recorded touch inputs. We demonstrate that significant touch accuracy improvements can be obtained when either raw sensor data is used as an input or when the device's reported touch location is used as an input, with the latter marginally outperforming the former. We show that learned offset functions are highly nonlinear and user-specific and that user-specific models outperform models trained on data pooled from several users. Crucially, significant performance improvements can be obtained with a small (≈200) number of training examples, easily obtained for a particular user through a calibration game or from keyboard entry data.