AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization
November 24, 2016 Β· Declared Dead Β· π International Conference on Human Factors in Computing Systems
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Authors
Byungjoo Lee, Mathieu Nancel, Sunjun Kim, Antti Oulasvirta
arXiv ID
1611.08154
Category
cs.HC: Human-Computer Interaction
Citations
25
Venue
International Conference on Human Factors in Computing Systems
Last Checked
4 months ago
Abstract
A well-designed control-to-display gain function can improve pointing performance with indirect pointing devices like trackpads. 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.
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