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CMU-HCII-10-104
Human-Computer Interaction Institute
School of Computer Science, Carnegie Mellon University
CMU-HCII-10-104
Using Data About Real World Pointing
Performance to Improve Computer Access with
Automatic Assessment
Amy Hurst
June 2010
Ph.D. Thesis
CMU-HCII-10-104.pdf
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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