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Daniel Buschek, Oliver Schoenleben, Antti Oulasvirta
Improving Accuracy in Back-of-Device Multitouch Typing: A Clustering-based Approach to Keyboard Updating
In IUI '14: Proceedings of the 19th International Conference on Intelligent User Interfaces. Haifa, Israel, February 24 - 27, 2014. ACM, New York, NY, USA.
doi: 10.1145/2557500.2557501 (bib)

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.