A Comparative Evaluation of Spatial Targeting Behaviour Patterns for Finger and Stylus Tapping on Mobile Touchscreen Devices

Abstract

Models of 2D targeting error patterns have been applied as a valuable computational tool for analysing finger touch behaviour on mobile devices, improving touch accuracy and inferring context. However, their use in stylus input is yet unexplored. This paper presents the first empirical study and analyses of such models for tapping with a stylus. In a user study (N = 28), we collected targeting data on a smartphone, both for stationary use (sitting) and walking. We compare targeting patterns between index finger input and three stylus variations -- two stylus widths and nib types as well as the addition of a hover cursor. Our analyses reveal that stylus targeting patterns are user-specific, and that offset models improve stylus tapping accuracy, but less so than for finger touch. Input method has a stronger influence on targeting patterns than mobility, and stylus width is more influential than the hover cursor. Stylus models improve finger accuracy as well, but not vice versa. The extent of the stylus accuracy advantage compared to the finger depends on screen location and mobility. We also discuss patterns related to mobility and gliding of the stylus on the screen. We conclude with implications for target sizes and offset model applications.

The Effects of Predictive Features of Mobile Keyboards on Text Entry Speed and Errors

“…Knowing what users intended to write is impossible [14]. Thus, previous studies of text entry "in the wild" [10,18] ignored error rates, as calculating error rates for uncontrolled text input is very challenging. Furthermore, privacy concerns limit the opportunities to collect data from personal contexts [9].…”