research-article

Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces

Authors:

Krzysztof Z. Gajos,

Jacob O. Wobbrock, and

Daniel S. WeldAuthors Info & Claims

CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2008

Pages 1257 - 1266

https://doi.org/10.1145/1357054.1357250

Published: 06 April 2008 Publication History

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Abstract

We evaluate two systems for automatically generating personalized interfaces adapted to the individual motor capabilities of users with motor impairments. The first system, SUPPLE, adapts to users' capabilities indirectly by first using the ARNAULD preference elicitation engine to model a user's preferences regarding how he or she likes the interfaces to be created. The second system, SUPPLE++, models a user's motor abilities directly from a set of one-time motor performance tests. In a study comparing these approaches to baseline interfaces, participants with motor impairments were 26.4% faster using ability-based user interfaces generated by SUPPLE++. They also made 73% fewer errors, strongly preferred those interfaces to the manufacturers' defaults, and found them more efficient, easier to use, and much less physically tiring. These findings indicate that rather than requiring some users with motor impairments to adapt themselves to software using separate assistive technologies, software can now adapt itself to the capabilities of its users.

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Andrew SBishop CTigwell G(2024)Light and Dark ModeProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435398:1(1-23)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643539
Jiang Y(2024)Computational Representations for Graphical User InterfacesExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3638191(1-6)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3638191
Jiang YZhou CGarg VOulasvirta A(2024)Graph4GUI: Graph Neural Networks for Representing Graphical User InterfacesProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642822(1-18)Online publication date: 11-May-2024
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Index Terms

Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
2. Social and professional topics
  1. Professional topics
    1. Computing profession
      1. Assistive technologies
  2. User characteristics
    1. People with disabilities

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Reviews

Reviewer: Mariana Damova

A contribution in the field of assisted computing for motor-impaired individuals, this paper presents experiments and evaluates the results from the use of two systems especially designed "to adapt user interfaces to the actual abilities of individual users with motor impairments." The purpose of the reported research is to verify usability and to compare acceptance by users of automatically generated user interfaces with that of commercially standard Windows user interfaces. The paper advances the idea that automatically generated interfaces are an important alternative to human-crafted interfaces because of the high cost of the latter and the variety of requirements for users with special needs. Creating cheaper and more easily adaptable interfaces is a step toward allowing all users equal access to computer facilities and regular activities. SUPPLE, one of the evaluated systems, uses "the ARNAULD preference elicitation engine to model a user's preferences regarding how he or she likes the interfaces to be created." The other evaluated system, SUPPLE++, "models a user's motor abilities directly from a set of one-time motor performance tests." The experiments are performed by 11 motor-impaired participants, with various conditions, and six able-bodied participants. The authors establish that all users perform better and prefer the ability-based interfaces; the preference-based interfaces rank second, and the standard baseline ones rank third. The paper shows, with a very detailed description of the methods and the calculation of the results, that evaluation takes place according to several parameters, such as widget manipulation time, interface navigation time, total time, error rate, and the following subjective criteria: ease of use, attractiveness, tiredness-producing, and efficiency. The ability-based and the preference-based interfaces score better than the baseline in all criteria except for attractiveness. The motor-impaired users were 8.4 to 42.2 percent faster with the ability-based interfaces. This study presents interesting insight in an intriguing field. The very promising results give hope that such applications will become fully accessible and usable after the prototypes become real systems. Supplied with extensive background research, comments, and illustrative pictures and graphs, this paper would be appropriate for those interested in applications for motor-impaired individuals and in interesting technological advances in general. Online Computing Reviews Service

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Information & Contributors

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Published In

CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2008

1870 pages

ISBN:9781605580111

DOI:10.1145/1357054

General Chairs:
Mary Czerwinski
Microsoft Research, USA
,
Arnie Lund
Microsoft, USA
,
Program Chair:
Desney Tan
Microsoft Research, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 06 April 2008

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CHI '08

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CHI '08: CHI Conference on Human Factors in Computing Systems

April 5 - 10, 2008

Florence, Italy

Acceptance Rates

CHI '08 Paper Acceptance Rate 157 of 714 submissions, 22%;

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

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CHI PLAY '24

Sponsor:
sigchi

The Annual Symposium on Computer-Human Interaction in Play

October 14 - 17, 2024

Tampere , Finland

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Jiang Y(2024)Computational Representations for Graphical User InterfacesExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3638191(1-6)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3638191
Jiang YZhou CGarg VOulasvirta A(2024)Graph4GUI: Graph Neural Networks for Representing Graphical User InterfacesProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642822(1-18)Online publication date: 11-May-2024
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Yamagami MPortnova-Fahreeva AKong JWobbrock JMankoff J(2023)How Do People with Limited Movement Personalize Upper-Body Gestures? Considerations for the Design of Personalized and Accessible Gesture InterfacesProceedings of the 25th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3597638.3608430(1-15)Online publication date: 22-Oct-2023
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Paulino DCorreia ABarroso JParedes H(2023)Cognitive personalization for online microtask labor platforms: A systematic literature reviewUser Modeling and User-Adapted Interaction10.1007/s11257-023-09383-wOnline publication date: 19-Sep-2023
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Howes AJokinen JOulasvirta A(2023)Towards machines that understand peopleAI Magazine10.1002/aaai.1211644:3(312-327)Online publication date: 14-Sep-2023
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Automatically generating user interfaces adapted to users' motor and vision capabilities

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