AutoGain: Gain Function Adaptation with Submovement Efficiency Optimization

Byungjoo Lee, Mathieu Nancel, Sunjun Kim, Antti Oulasvirta

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationCHI '20: Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems
PublisherACM
Number of pages12
ISBN (Electronic)978-1-4503-6708-0
DOIs
Publication statusPublished - 21 Apr 2020
MoE publication typeA4 Article in a conference publication
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Honolulu, United States
Duration: 26 Apr 202030 Apr 2020
https://chi2020.acm.org/

Conference

ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
CountryUnited States
CityHonolulu
Period26/04/202030/04/2020
Internet address

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