Extending Fitts' law to two-dimensional tasks
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Beyond Fitts' law: models for trajectory-based HCI tasks
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Refining Fitts' law models for bivariate pointing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
International Journal of Human-Computer Studies - Special issue: Fitts law 50 years later: Applications and contributions from human-computer interaction
An error model for pointing based on Fitts' law
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Fitts' law as a research and design tool in human-computer interaction
Human-Computer Interaction
The performance of touch screen soft buttons
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Quasi-qwerty soft keyboard optimization
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
Effects of motor scale, visual scale, and quantization on small target acquisition difficulty
ACM Transactions on Computer-Human Interaction (TOCHI)
International Journal of Human-Computer Studies
The word-gesture keyboard: reimagining keyboard interaction
Communications of the ACM
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
Bayesian touch: a statistical criterion of target selection with finger touch
Proceedings of the 26th annual ACM symposium on User interface software and technology
Two touch system latency estimators: high accuracy and low overhead
Proceedings of the 2013 ACM international conference on Interactive tabletops and surfaces
Hi-index | 0.01 |
Fitts' law has proven to be a strong predictor of pointing performance under a wide range of conditions. However, it has been insufficient in modeling small-target acquisition with finger-touch based input on screens. We propose a dual-distribution hypothesis to interpret the distribution of the endpoints in finger touch input. We hypothesize the movement endpoint distribution as a sum of two independent normal distributions. One distribution reflects the relative precision governed by the speed-accuracy tradeoff rule in the human motor system, and the other captures the absolute precision of finger touch independent of the speed-accuracy tradeoff effect. Based on this hypothesis, we derived the FFitts model - an expansion of Fitts' law for finger touch input. We present three experiments in 1D target acquisition, 2D target acquisition and touchscreen keyboard typing tasks respectively. The results showed that FFitts law is more accurate than Fitts' law in modeling finger input on touchscreens. At 0.91 or a greater R2 value, FFitts' index of difficulty is able to account for significantly more variance than conventional Fitts' index of difficulty based on either a nominal target width or an effective target width in all the three experiments.