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AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization

Byungjoo Lee 1 Mathieu Nancel 2 Sunjun Kim 1 Antti Oulasvirta 3
2 LOKI - Technology and knowledge for interaction
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
Abstract : 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.
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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⟩

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