Accuracy measures for evaluating computer pointing devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
User Models and User Physical Capability
User Modeling and User-Adapted Interaction
Cursor measures for motion-impaired computer users
Proceedings of the fifth international ACM conference on Assistive technologies
Mouse movements of motion-impaired users: a submovement analysis
Assets '04 Proceedings of the 6th international ACM SIGACCESS conference on Computers and accessibility
International Journal of Human-Computer Studies - Special issue: Fitts law 50 years later: Applications and contributions from human-computer interaction
"Beating" Fitts' law: virtual enhancements for pointing facilitation
International Journal of Human-Computer Studies - Special issue: Fitts law 50 years later: Applications and contributions from human-computer interaction
Learning from preschool children's pointing sub-movements
Proceedings of the 2006 conference on Interaction design and children
PointAssist: helping four year olds point with ease
IDC '08 Proceedings of the 7th international conference on Interaction design and children
Pointassist for older adults: analyzing sub-movement characteristics to aid in pointing tasks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Individuals with upper limb impairments due to cerebral palsy encounter difficulties when using pointing devices and can be limited in communicating and accessing education tools through computers. Analysis of cursor trajectories can identify some of the factors limiting cursor movement, and provide a better understanding of human movement to assist in designing accessible computer interfaces. This study evaluated cursor trajectories from 29 individuals with bilateral cerebral palsy (CP) and different levels of function. The functional level was classified based on the MACS (Manual Ability Classification System). Results show that the contributors to a model that assesses different MACS levels are the movement time, acceleration-deceleration cycles and average speed. The model appears unaffected by accuracy measures. For both typically-developed youth and participants with CP, a good model of index of difficulty must include the following predictors: rapidity - movement time, average speed, zero acceleration crossings and accuracy, trajectory distance, linearity index, and indices of vertical and horizontal components. Models for those who are typically-developed should also include an index of diagonal component and curvature index.