Article

Accuracy measures for evaluating computer pointing devices

Authors:

I. Scott MacKenzie,

Tatu Kauppinen,

Miika SilfverbergAuthors Info & Claims

CHI '01: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 9 - 16

https://doi.org/10.1145/365024.365028

Published: 01 March 2001 Publication History

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Abstract

In view of the difficulties in evaluating computer pointing devices across different tasks within dynamic and complex systems, new performance measures are needed. This paper proposes seven new accuracy measures to elicit (sometimes subtle) differences among devices in precision pointing tasks. The measures are target re-entry, task axis crossing, movement direction change, orthogonal direction change, movement variability, movement error, and movement offset. Unlike movement time, error rate, and throughput, which are based on a single measurement per trial, the new measures capture aspects of movement behaviour during a trial. The theoretical basis and computational techniques for the measures are described, with examples given. An evaluation with four pointing devices was conducted to validate the measures. A causal relationship to pointing device efficiency (viz. throughput) was found, as was an ability to discriminate among devices in situations where differences did not otherwise appear. Implications for pointing device research are discussed.

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Index Terms

Accuracy measures for evaluating computer pointing devices
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
    2. Interaction devices
      1. Touch screens

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Published In

CHI '01: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

March 2001

559 pages

ISBN:1581133278

DOI:10.1145/365024

Chairmen:
Julie Jacko
Georgia Institute of Technology
,
Andrew Sears
UMBC

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Association for Computing Machinery

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Published: 01 March 2001

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CHI '01 Paper Acceptance Rate 69 of 352 submissions, 20%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

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https://dl.acm.org/doi/10.1145/3677386.3682101
Kong JZhong MFogarty JWobbrock J(2024)The Ability-Based Design Mobile Toolkit (ABD-MT): Developer Support for Runtime Interface Adaptation Based on Users' AbilitiesProceedings of the ACM on Human-Computer Interaction10.1145/36765248:MHCI(1-26)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.1145/3676524
Mitchell CWobbrock J(2024)Characterizing "Motor Ability" for Ability-Based DesignProceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3663548.3675646(1-15)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3663548.3675646
Islam MSojib NKabir IAmit AAmin MBillah S(2024)Wheeler: A Three-Wheeled Input Device for Usable, Efficient, and Versatile Non-Visual InteractionProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676396(1-20)Online publication date: 13-Oct-2024
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Kang DKim NKang DYoon JKim SLee B(2024)Quantifying Wrist-Aiming Habits with A Dual-Sensor Mouse: Implications for Player Performance and WorkloadProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642797(1-18)Online publication date: 11-May-2024
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Tang JWeiss HStirling L(2023)A comparison of sensorimotor assessment accuracy between augmented reality and touchscreen environmentsProceedings of the Human Factors and Ergonomics Society Annual Meeting10.1177/2169506723119254767:1(1532-1536)Online publication date: 20-Oct-2023
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