Computational Workflows for Designing Input Devices

Yi Chi Liao*

*Corresponding author for this work

Research output: Contribution to conferenceAbstractScientificpeer-review

Abstract

Input devices, such as buttons and sliders, are the foundation of any interface. The typical user-centered design workflow requires the developers and users to go through many iterations of design, implementation, and analysis. The procedure is inefficient, and human decisions highly bias the results. While computational methods are used to assist various design tasks, there has not been any holistic approach to automate the design of input components. My thesis proposed a series of Computational Input Design workflows: I envision a sample-efficient multi-objective optimization algorithm that cleverly selects design instances, which are instantly deployed on physical simulators. A meta-reinforcement learning user model then simulates the user behaviors when using the design instance upon the simulators. The new workflows derive Pareto-optimal designs with high efficiency and automation. I demonstrate designing a push-button via the proposed methods. The resulting designs outperform the known baselines. The Computational Input Design process can be generalized to other devices, such as joystick, touchscreen, mouse, controller, etc.

Original languageEnglish
Number of pages6
DOIs
Publication statusPublished - May 2021
MoE publication typeNot Eligible
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Virtual, Online, Japan
Duration: 8 May 202113 May 2021
https://chi2021.acm.org/

Conference

ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
Country/TerritoryJapan
CityVirtual, Online
Period08/05/202113/05/2021
Internet address

Keywords

  • Bayesian optimization
  • Button
  • Computational methods
  • Design workflow
  • Input devices
  • Meta learning
  • Meta-RL
  • Physical simulator
  • Reinforcement learning

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