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.

Full Thesis:
Download Amy Hurst’s Full Thesis

Thesis Advisor:
Jennifer Mankoff and Scott Hudson

Award Date:
September 1, 2010

Carnegie Mellon
Pittsburgh, USA

Author Contact: