C4.5: programs for machine learning
C4.5: programs for machine learning
Making computers easier for older adults to use: area cursors and sticky icons
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
More than dotting the i's --- foundations for crossing-based interfaces
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The bubble cursor: enhancing target acquisition by dynamic resizing of the cursor's activation area
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Effect of age and Parkinson's disease on cursor positioning using a mouse
Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Proceedings of the 7th international ACM SIGACCESS conference on Computers and accessibility
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Dynamic detection of novice vs. skilled use without a task model
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Endpoint prediction using motion kinematics
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 20th annual ACM symposium on User interface software and technology
Understanding pointing problems in real world computing environments
Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
ACM Transactions on Accessible Computing (TACCESS)
Automatic assessment and adaptation to real world pointing performance
ACM SIGACCESS Accessibility and Computing
The potential of adaptive interfaces as an accessibility aid for older web users
Proceedings of the 2010 International Cross Disciplinary Conference on Web Accessibility (W4A)
Towards accessible interactions with pervasive interfaces, based on human capabilities
ICCHP'10 Proceedings of the 12th international conference on Computers helping people with special needs: Part I
Ability-Based Design: Concept, Principles and Examples
ACM Transactions on Accessible Computing (TACCESS)
Instrumenting the crowd: using implicit behavioral measures to predict task performance
Proceedings of the 24th annual ACM symposium on User interface software and technology
Personalized dynamic accessibility
interactions
Accurate measurements of pointing performance from in situ observations
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Drag and drop the apple: the semantic weight of words and images in touch-based interaction
Proceedings of the 7th International Conference on Tangible, Embedded and Embodied Interaction
PointAssist: assisting individuals with motor impairments
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using behavioral data to identify interviewer fabrication in surveys
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Distinguishing Users By Pointing Performance in Laboratory and Real-World Tasks
ACM Transactions on Accessible Computing (TACCESS)
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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.