Human-Computer Interaction Institute
School of Computer Science, Carnegie Mellon University


Using Data About Real World Pointing
Performance to Improve Computer Access with
Automatic Assessment

Amy Hurst

June 2010

Ph.D. Thesis


Keywords: Assistive Technology, Computer Accessibility, Motor Impairments, Data Collection in the Wild, Pointing Input, Measurement, Performance, Human Factors, Usability Analysis, Target Identification

Accurate pointing is an obstacle to computer access for individuals with motor impairments. One of the main barriers to assisting individuals with pointing problems is a lack of frequent and low-cost assessment of those pointing problems. We are working to build technology to automatically assess pointing problems during every day (or real world) computer use. To this end, we have studied real world pointing use from older adults and individuals with motor impairments and developed novel techniques to analyze their performance. Our investigation contributes to a better understanding of real world pointing performance, and how to assess pointing performance with machine learning.

160 pages

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