AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization

Honorable Mention
Hand Gesture RecognitionComputational Methods in HCIAI/ML Researchers & EngineersHCI Researchers

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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https://hci.top/en/papers/chi/32050/2020

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DOI: https://doi.org/10.1145/3313831.3376244
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Source
CHI
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Year
2020
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Award
Honorable Mention
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Authors
4 authors
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Subtopics
Hand Gesture Recognition, Computational Methods in HCI
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Professions
AI/ML Researchers & Engineers, HCI Researchers
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Content Status
Abstract only
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