Target size study for one-handed thumb use on small touchscreen devices
Proceedings of the 8th conference on Human-computer interaction with mobile devices and services
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Shift: a technique for operating pen-based interfaces using touch
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
TapTap and MagStick: improving one-handed target acquisition on small touch-screens
AVI '08 Proceedings of the working conference on Advanced visual interfaces
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
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
Calibration games: making calibration tasks enjoyable by adding motivating game elements
Proceedings of the 24th annual ACM symposium on User interface software and technology
Towards online adaptation and personalization of key-target resizing for mobile devices
Proceedings of the 2012 ACM international conference on Intelligent User Interfaces
Personalized input: improving ten-finger touchscreen typing through automatic adaptation
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
Sparse selection of training data for touch correction systems
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
Proceedings of the 19th international conference on Intelligent User Interfaces
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We present a machine learning approach to train user-specific offset models, which map actual to intended touch locations to improve accuracy. We propose a flexible framework to adapt and apply models trained on touch data from one device and user to others. This paper presents a study of the first published experimental data from multiple devices per user, and indicates that models not only improve accuracy between repeated sessions for the same user, but across devices and users, too. Device-specific models outperform unadapted user-specific models from different devices. However, with both user- and device-specific data, we demonstrate that our approach allows to combine this information to adapt models to the targeted device resulting in significant improvement. On average, adapted models improved accuracy by over 8%. We show that models can be obtained from a small number of touches (≈60). We also apply models to predict input-styles and identify users.