AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization

TL;DR

AutoGain is a novel method that personalizes gain functions for indirect pointing devices by optimizing submovement trajectories, significantly improving pointing efficiency within half an hour of use and over extended periods.

Contribution

AutoGain introduces a submovement-level optimization technique for automatic gain function adaptation, enabling personalized and improved pointing performance without prior reference functions.

Findings

AutoGain achieves comparable performance to commercial gain functions in less than 30 minutes.

AutoGain effectively adapts to new input devices like Leap Motion without reference functions.

Participants showed performance improvements over a month of using AutoGain.

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.