Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
LOF: identifying density-based local outliers
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A technique for computer detection and correction of spelling errors
Communications of the ACM
Language modeling for soft keyboards
Proceedings of the 7th international conference on Intelligent user interfaces
An empirical study of smoothing techniques for language modeling
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Relative keyboard input system
Proceedings of the 13th international conference on Intelligent user interfaces
The performance of hand postures in front- and back-of-device interaction for mobile computing
International Journal of Human-Computer Studies
Empirical evaluation for finger input properties in multi-touch interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Back-of-device interaction allows creating very small touch devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Double-side multi-touch input for mobile devices
CHI '09 Extended Abstracts on Human Factors in Computing Systems
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Natural Language Processing with Python
Natural Language Processing with Python
Usability guided key-target resizing for soft keyboards
Proceedings of the 15th international conference on Intelligent user interfaces
RearType: text entry using keys on the back of a device
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Typing on flat glass: examining ten-finger expert typing patterns on touch surfaces
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
A versatile dataset for text entry evaluations based on genuine mobile emails
Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services
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
Back keyboard: a physical keyboard on backside of mobile phone using qwerty
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Does proprioception guide back-of-device pointing as well as vision?
CHI '12 Extended Abstracts on Human Factors in Computing Systems
Touch behavior with different postures on soft smartphone keyboards
MobileHCI '12 Proceedings of the 14th international conference on Human-computer interaction with mobile devices and services
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
Improving two-thumb text entry on touchscreen devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ContextType: using hand posture information to improve mobile touch screen text entry
Proceedings of the SIGCHI Conference 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
Proceedings of the 15th international conference on Human-computer interaction with mobile devices and services
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Recent work has shown that a multitouch sensor attached to the back of a handheld device can allow rapid typing engaging all ten fingers. However, high error rates remain a problem, because the user can not see or feel key-targets on the back. We propose a machine learning approach that can significantly improve accuracy. The method considers hand anatomy and movement ranges of fingers. The key insight is a combination of keyboard and hand models in a hierarchical clustering method. This enables dynamic re-estimation of key-locations while typing to account for changes in hand postures and movement ranges of fingers. We also show that accuracy can be further improved with language models. Results from a user study show improvements of over 40% compared to the previously deployed "naive" approach. We examine entropy as a touch precision metric with respect to typing experience. We also find that the QWERTY layout is not ideal. Finally, we conclude with ideas for further improvements.