research-article

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

Authors:

Krzysztof Z. Gajos,

Jacob O. Wobbrock,

Daniel S. WeldAuthors Info & Claims

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

Pages 1257 - 1266

https://doi.org/10.1145/1357054.1357250

Published: 06 April 2008 Publication History

Get Access

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.

References

[1]

Akamatsu, M., MacKenzie, I. S., and Hasbroucq, T. A comparison of tactile, auditory, and visual feedback in a pointing task using a mouse-type device. Ergonomics, 38, 4 (1995), 816--827.

Crossref

Google Scholar

[2]

Bergman, E. and Johnson, E. Towards Accessible Human-Computer Interaction. Advances in Human-Computer Interaction, 5, 1.

Digital Library

Google Scholar

[3]

Bigham, J. P., Kaminsky, R. S., Ladner, R. E., Danielsson, O. M., and Hempton, G. L. Webinsight:: making web images accessible. Proc. Assets '06. ACM Press, New York, NY, USA, 2006, 181--188.

Digital Library

Google Scholar

[4]

Carter, S., Hurst, A., Mankoff, J., and Li, J. Dynamically adapting GUIs to diverse input devices. Proc. Assets '06. ACM Press, New York, NY, USA, 2006, 63--70.

Digital Library

Google Scholar

[5]

Dawe, M. Desperately seeking simplicity: how young adults with cognitive disabilities and their families adopt assistive technologies. Proc. CHI'06. (2006), 1143--1152.

Digital Library

Google Scholar

[6]

Fichten, C., Barile, M., Asuncion, J., and Fossey, M. What government, agencies, and organizations can do to improve access to computers for postsecondary students with disabilities: recommendations based on Canadian empirical data. Int J Rehabil Res, 23, 3 (2000), 191--9.

Crossref

Google Scholar

[7]

Gajos, K., Christianson, D., Hoffmann, R., Shaked, T., Henning, K., Long, J. J., and Weld, D. S. Fast and robust interface generation for ubiquitous applications. Proc. Ubicomp'05. Tokyo, Japan, 2005.

Digital Library

Google Scholar

[8]

Gajos, K. and Weld, D. S. Supple: automatically generating user interfaces. Proc. IUI'04. ACM Press, New York, NY, 2004, 93--100.

Digital Library

Google Scholar

[9]

Gajos, K. and Weld, D. S. Preference elicitation for interface optimization. Proc. UIST '05. ACM Press, New York, NY, 2005.

Digital Library

Google Scholar

[10]

Gajos, K. Z., Wobbrock, J. O., and Weld, D. S. Automatically generating user interfaces adapted to users' motor and vision capabilities. Proc. UIST'07. ACM Press, New York, NY, 2007.

Digital Library

Google Scholar

[11]

Harada, S., Wobbrock, J. O., and Landay, J. A. Voicedraw: A voice-driven hands-free drawing application. Proc. ASSETS'07. ACM Press, 2007.

Digital Library

Google Scholar

[12]

Hornof, A., Cavender, A., and Hoselton, R. Eyedraw: a system for drawing pictures with eye movements. Proc. ASSETS'04. ACM Press, New York, NY, 2004, 86--93.

Digital Library

Google Scholar

[13]

Hwang, F., Keates, S., Langdon, P., and Clarkson, J. Mouse movements of motion-impaired users: a submovement analysis. Proc. Assets '04. ACM Press, New York, NY, USA, 2004, 102--109.

Digital Library

Google Scholar

[14]

International Organization for Standardization. 9241-9 Ergonomic requirements for office work with visual display terminals (VDTs)-Part 9: Requirements for non-keyboard input devices (2000).

Google Scholar

[15]

Keates, S., Langdon, P., Clarkson, J. P., and Robinson, P. User models and user physical capability. User Modeling and User-Adapted Interaction, 12, 2 (2002), 139--169.

Digital Library

Google Scholar

[16]

Koester, H. Abandonment of speech recognition by new users. Proc. RESNA03. Atlanta, Georgia, 2003.

Google Scholar

[17]

Law, C., Sears, A., and Price, K. Issues in the categorization of disabilities for user testing. Proc. HCII'05. 2005.

Google Scholar

[18]

Littell, R., MIlliken, G., Stroup, W., and Wolfinger, R. SAS System for Mixed Models. SAS Institute, Inc., Cary, NC, 1996.

Google Scholar

[19]

Mankoff, J., Dey, A., Batra, U., and Moore, M. Web accessibility for low bandwidth input. Proc. Assets '02. ACM Press, New York, NY, USA, 2002, 17--24.

Digital Library

Google Scholar

[20]

Nichols, J., Myers, B. A., Higgins, M., Hughes, J., Harris, T. K., Rosenfeld, R., and Pignol, M. Generating remote control interfaces for complex appliances. Proc. UIST'02. Paris, France, 2002.

Digital Library

Google Scholar

[21]

Phillips, B. and Zhao, H. Predictors of assistive technology abandonment. Assist Technol, 5, 1 (1993), 36--45.

Crossref

Google Scholar

[22]

Savidis, A. Dynamic software assembly for automatic deployment-oriented adaptation. Electronic Notes in Theoretical Computer Science, 127, 3 (2005), 207--217.

Digital Library

Google Scholar

[23]

Savidis, A., Antona, M., and Stephanidis, C. A decision-making specification language for verifiable user-interface adaptation logic. International Journal of Software Engineering And Knowledge Engineering, 15, 6 (2005), 1063 -- 1094.

Crossref

Google Scholar

[24]

Savidis, A. and Stephanidis, C. Inclusive development: Software engineering requirements for universally accessible interactions. Interacting with Computers, 18, 1 (2006), 71--116.

Digital Library

Google Scholar

[25]

Schuster, C. and Von Eye, A. The relationship of anova models with random effects and repeated measurement designs. Journal of Adolescent Research, 16, 2 (2001), 205--220.

Crossref

Google Scholar

[26]

Stephanidis, C. User interfaces for all: New perspectives into human-computer interaction. In C. Stephanidis, ed., User Interfaces for All, Lawrence Erlbaum, 2001. 3--17.

Google Scholar

[27]

Vermunt, J. K. Log-linear Models for Event Histories. Sage Publications, 1997.

Google Scholar

[28]

Wilcoxon, F. Individual comparisons by ranking methods. Biometrics Bulletin, 1, 6 (1945), 80--83.

Crossref

Google Scholar

[29]

Winship, C. and Mare, R. D. Regression models with ordinal variables. American Sociological Review, 49, 4 (1984), 512--525.

Crossref

Google Scholar

Cited By

View all

Saboundji RFaragó KFiryaridi V(2024)Prediction of Attention Groups and Big Five Personality Traits from Gaze Features Collected from an Outlier Search GameJournal of Imaging10.3390/jimaging1010025510:10(255)Online publication date: 16-Oct-2024
https://doi.org/10.3390/jimaging10100255
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
Saif AZakir MRaza ANaseem M(2024)EvolveUI: User Interfaces that Evolve with User ProficiencyProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675078(230-237)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3674829.3675078
Show More Cited By

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

Recommendations

Automatically generating personalized user interfaces with Supple

Today's computer-human interfaces are typically designed with the assumption that they are going to be used by an able-bodied person, who is using a typical set of input and output devices, who has typical perceptual and cognitive abilities, and who is ...
Automatically generating user interfaces adapted to users' motor and vision capabilities
UIST '07: Proceedings of the 20th annual ACM symposium on User interface software and technology

Most of today's GUIs are designed for the typical, able-bodied user; atypical users are, for the most part, left to adapt as best they can, perhaps using specialized assistive technologies as an aid. In this paper, we present an alternative approach: ...
Automatically generating custom user interfaces for users with physical disabilities
Assets '06: Proceedings of the 8th international ACM SIGACCESS conference on Computers and accessibility

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

Access critical reviews of Computing literature here

Become a reviewer for Computing Reviews.

Comments

Information & Contributors

Information

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]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 April 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CHI '08

Sponsor:

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%

Upcoming Conference

CHI 2025

Sponsor:
sigchi

ACM CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

133
Total Citations
View Citations
2,388
Total Downloads

Downloads (Last 12 months)129
Downloads (Last 6 weeks)10

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Saboundji RFaragó KFiryaridi V(2024)Prediction of Attention Groups and Big Five Personality Traits from Gaze Features Collected from an Outlier Search GameJournal of Imaging10.3390/jimaging1010025510:10(255)Online publication date: 16-Oct-2024
https://doi.org/10.3390/jimaging10100255
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
Saif AZakir MRaza ANaseem M(2024)EvolveUI: User Interfaces that Evolve with User ProficiencyProceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies10.1145/3674829.3675078(230-237)Online publication date: 8-Jul-2024
https://dl.acm.org/doi/10.1145/3674829.3675078
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
Mack KGlazko KIslam JHofmann MMankoff J(2024)"It's like Goldilocks:" Bespoke Slides for Fluctuating Audience Access NeedsProceedings of the 26th International ACM SIGACCESS Conference on Computers and Accessibility10.1145/3663548.3675640(1-15)Online publication date: 27-Oct-2024
https://dl.acm.org/doi/10.1145/3663548.3675640
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 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 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642822(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642822
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
https://dl.acm.org/doi/10.1145/3597638.3608430
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-w34:3(617-658)Online publication date: 19-Sep-2023
https://doi.org/10.1007/s11257-023-09383-w
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Automatically generating personalized user interfaces with Supple

Automatically generating user interfaces adapted to users' motor and vision capabilities

Automatically generating custom user interfaces for users with physical disabilities

Reviews

Access critical reviews of Computing literature here