ABSTRACT
Accurately predicting the accuracy of finger-touch target acquisition is crucial for designing touchscreen UI and for modeling complex and higher level touch interaction behaviors. Despite its importance, there has been little theoretical work on creating such models. Building on the Dual Gaussian Distribution Model[3], we derived an accuracy model that predicts the success rate of target acquisition based on the target size. We evaluated the model by comparing the predicted success rates with empirical measures for three types of targets including 1-dimensional vertical and horizontal, and 2-dimensional circular targets. The predictions matched the empirical data very well: the differences between predicted and observed success rates were under 5% for 4.8 mm and 7.2 mm targets, and under 10% for 2.4 mm targets. The evaluation results suggest that our simple model can reliably predict touch accuracy.
Supplemental Material
- Material design for Android, https://www.google.com/design/spec/layout/metricskeylines.htmlmetrics-keylines-touch-target-size.Google Scholar
- Azenkot, S., and Zhai, S. Touch behavior with different postures on soft smartphone keyboards. In Proceedings of the 14th International Conference on Human-computer Interaction with Mobile Devices and Services, MobileHCI '12, ACM (New York, NY, USA, 2012), 251--260. Google ScholarDigital Library
- Bi, X., Li, Y., and Zhai, S. FFitts law: Modeling finger touch with Fitts' law. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '13, ACM (New York, NY, USA, 2013), 1363--1372. Google ScholarDigital Library
- Bi, X., and Zhai, S. Bayesian touch: A statistical criterion of target selection with finger touch. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology, UIST '13, ACM (New York, NY, USA, 2013), 51--60. Google ScholarDigital Library
- Fitts, P. M. The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 47, 6 (1954), 381--391. Google ScholarCross Ref
- Henze, N., Rukzio, E., and Boll, S. 100,000,000 taps: Analysis and improvement of touch performance in the large. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services, MobileHCI '11, ACM (New York, NY, USA, 2011), 133--142. Google ScholarDigital Library
- Henze, N., Rukzio, E., and Boll, S. Observational and experimental investigation of typing behaviour using virtual keyboards for mobile devices. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '12, ACM (New York, NY, USA, 2012), 2659--2668. Google ScholarDigital Library
- Hick, W. E. On the rate of gain of information. Quarterly Journal of Experimental Psychology 4 (1952), 11--26. Google ScholarCross Ref
- Holz, C., and Baudisch, P. The generalized perceived input point model and how to double touch accuracy by extracting fingerprints. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '10, ACM (New York, NY, USA, 2010), 581--590. Google ScholarDigital Library
- Holz, C., and Baudisch, P. Understanding touch. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '11, ACM (New York, NY, USA, 2011), 2501--2510. Google ScholarDigital Library
- Nichols, J., and Faulring, A. Automatic interface generation and future user interface tools. In Tools ACM CHI 2005 Workshop on The Future of User Interface Design Tools (2005).Google Scholar
- Schwarz, J., Hudson, S., Mankoff, J., and Wilson, A. D. A framework for robust and flexible handling of inputs with uncertainty. In Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology, UIST '10, ACM (New York, NY, USA, 2010), 47--56. Google ScholarDigital Library
- Wang, F., and Ren, X. Empirical evaluation for finger input properties in multi-touch interaction. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '09, ACM (New York, NY, USA, 2009), 1063--1072. Google ScholarDigital Library
- Williamson, J. Continuous Uncertain Interaction. PhD thesis, The University of Glasgow, 2006.Google Scholar
- Wobbrock, J. O., Cutrell, E., Harada, S., and MacKenzie, I. S. An error model for pointing based on Fitts' law. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '08, ACM (New York, NY, USA, 2008), 1613--1622. Google ScholarDigital Library
- Zhai, S., Kong, J., and Ren, X. Speed-accuracy tradeoff in Fitts' law tasks: On the equivalency of actual and nominal pointing precision. Int. J. Hum.-Comput. Stud. 61, 6 (Dec. 2004), 823--856. Google ScholarDigital Library
Index Terms
- Predicting Finger-Touch Accuracy Based on the Dual Gaussian Distribution Model
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