AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization

Byungjoo Lee (1) , Mathieu Nancel (2) , Sunjun Kim (1) , Antti Oulasvirta (3)

1 KAIST - Korea Advanced Institute of Science and Technology
2 LOKI - Technology and knowledge for interaction
3 Aalto University

A well-designed control-to-display gain function can improve pointing performance with indirect pointing devices like track-pads. However, the design of gain functions is challenging and mostly based on trial and error. AutoGain is a novel method to individualize a gain function for indirect pointing devices in contexts where cursor trajectories can be tracked. It gradually improves pointing efficiency by using a novel submovement-level tracking+optimization technique that minimizes aiming error (undershooting/overshooting) for each submovement. We first show that AutoGain can produce, from scratch, gain functions with performance comparable to commercial designs, in less than a half-hour of active use. Second, we demonstrate AutoGain's applicability to emerging input devices (here, a Leap Motion controller) with no reference gain functions. Third, a one-month longitudinal study of normal computer use with AutoGain showed performance improvements from participants' default functions.

Mots clés: Pointing; Submovement; CD gain functions; Pointer acceleration; Human Performance; Pointing facilitation

Domaines: Interface homme-machine [cs.HC]

Byungjoo Lee, Mathieu Nancel, Sunjun Kim, Antti Oulasvirta. AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (CHI '20), Apr 2020, Honolulu, United States. pp.1-12, ⟨10.1145/3313831.3376244⟩. ⟨hal-02918581⟩