FFitts Law: Modeling Finger Touch with Fitts’ Law
Venue
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI 2013), ACM, New York, NY, USA, pp. 1363-1372
Publication Year
2013
Authors
Xiaojun Bi, Yang Li, Shumin Zhai
BibTeX
Abstract
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.
