Automatically detecting pointing performance

Amy Hurst, Scott E. Hudson, Jennifer Mankoff, Shari Trewin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Since not all persons interact with computer systems in the same way, computer systems should not interact with all individuals in the same way. This paper presents a significant step in automatically detecting characteristics of persons with a wide range of abilities based on observing their user input events. Three datasets are used to build learned statistical models on pointing data collected in a laboratory setting from individuals with varying ability to use computer pointing devices. The first dataset is used to distinguish between pointing behaviors from individuals with pointing problems vs. individuals without with 92.7% accuracy. The second is used to distinguish between pointing data from Young Adults and Adults vs. Older Adults vs. individuals with Parkinson's Disease with 91.6% accuracy. The final data set is used to predict the need for a specific adaptation based on a user's performance with 94.4% accuracy. These results suggest that it may be feasible to use such models to automatically identify computer users who would benefit from accessibility tools, and to even make specific tool recommendations.

Original languageEnglish (US)
Title of host publicationProceedings of the 13th International Conference on Intelligent User Interfaces 2008, IUI'08
Pages11-19
Number of pages9
DOIs
StatePublished - 2008
Event13th International Conference on Intelligent User Interfaces 2008, IUI'08 - Maspalomas, Gran Canaria, Spain
Duration: Jan 13 2008Jan 16 2008

Publication series

NameInternational Conference on Intelligent User Interfaces, Proceedings IUI

Conference

Conference13th International Conference on Intelligent User Interfaces 2008, IUI'08
CountrySpain
CityMaspalomas, Gran Canaria
Period1/13/081/16/08

Keywords

  • Accessibility
  • Activity recognition
  • Interaction techniques
  • Learned statistical models
  • Motor impairments

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction

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  • Cite this

    Hurst, A., Hudson, S. E., Mankoff, J., & Trewin, S. (2008). Automatically detecting pointing performance. In Proceedings of the 13th International Conference on Intelligent User Interfaces 2008, IUI'08 (pp. 11-19). [1378776] (International Conference on Intelligent User Interfaces, Proceedings IUI). https://doi.org/10.1145/1378773.1378776