We evaluate two methods for automatically generating personalized interfaces adapted to the individual motor capabilities of users with motor impairments. The first system, SUPPLE, adapts to user's capabilities indirectly by first using ARNAULD preference elicitation engine to model user's preferences regarding how he or she likes the interfaces to be created. The second, SUPPLE++, models a user's motor abilities directly through a set of one-time motor performance tests. In a study comparing these approaches to baseline interfaces, our results show that users with motor impairments were much faster and strongly preferred SUPPLE++ ability-based interfaces. Specifically, motor-impared participants were 26.4% faster using interfaces generated by SUPPLE++. They made 73% fewer errors, strongly preferred those interfaces to the manufacturers' defaults, and found them more efficient, easier to use, and much less physically tiring. These findings indicate that rather than requiring some users with motor impairments to adapt themselves to software using separate assistive technologies, software can now adapt itself to the capabilities of its users.
Krzysztof Z. Gajos, Jacob O. Wobbrock, and Daniel S. Weld. Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces. In CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pages 1257-1266, New York, NY, USA, 2008. ACM.