Elements of information theory
Elements of information theory
Beyond Fitts' law: models for trajectory-based HCI tasks
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Accuracy measures for evaluating computer pointing devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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
Behind Fitts' law: kinematic patterns in goal-directed movements
International Journal of Human-Computer Studies - Special issue: Fitts law 50 years later: Applications and contributions from human-computer interaction
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Fitts' law as a research and design tool in human-computer interaction
Human-Computer Interaction
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
Camera canvas: image editing software for people with disabilities
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: applications and services - Volume Part IV
| Hi-index | 0.00 |
In this paper, we propose the Relative Trajectory Information (RTI) measure, an information theoretic measure to evaluate mouse pointer trajectories. The measure is used to score the level of smoothness of mouse pointer trajectories. We show that, by leveraging Gaussian processes and information theory, RTI accounts for relative differences in timestamps of the mouse pointer trajectories. RTI also does not require explicit descriptions of targets, in either their location or size. Our experimental analysis shows how RTI can capture the motion signature of a user with severe motion disabilities and distinguish it from the motion signature of smooth trajectories obtained in a control experiment.