Extending Fitts' law to two-dimensional tasks
CHI '92 Proceedings of the 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
Refining Fitts' law models for bivariate pointing
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
PEBL: positive example based learning for Web page classification using SVM
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic approach to modeling two-dimensional pointing
ACM Transactions on Computer-Human Interaction (TOCHI)
Learning from positive and unlabeled examples
Theoretical Computer Science - Algorithmic learning theory (ALT 2000)
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
ACM Transactions on Accessible Computing (TACCESS)
Automatically detecting pointing performance
Proceedings of the 13th international conference on Intelligent user interfaces
Learning classifiers from only positive and unlabeled data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding pointing problems in real world computing environments
Proceedings of the 10th international ACM SIGACCESS conference on Computers and accessibility
Fitts' law as a research and design tool in human-computer interaction
Human-Computer Interaction
Automatically identifying targets users interact with during real world tasks
Proceedings of the 15th international conference on Intelligent user interfaces
Automatically generating personalized user interfaces with Supple
Artificial Intelligence
Instrumenting the crowd: using implicit behavioral measures to predict task performance
Proceedings of the 24th annual ACM symposium on User interface software and technology
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Drag and drop the apple: the semantic weight of words and images in touch-based interaction
Proceedings of the 7th International Conference on Tangible, Embedded and Embodied Interaction
CrowdLearner: rapidly creating mobile recognizers using crowdsourcing
Proceedings of the 26th annual ACM symposium on User interface software and technology
Distinguishing Users By Pointing Performance in Laboratory and Real-World Tasks
ACM Transactions on Accessible Computing (TACCESS)
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We present a method for obtaining lab-quality measurements of pointing performance from unobtrusive observations of natural in situ interactions. Specifically, we have developed a set of user-independent classifiers for discriminating between deliberate, targeted mouse pointer movements and those movements that were affected by any extraneous factors. To develop and validate these classifiers, we developed logging software to unobtrusively record pointer trajectories as participants naturally interacted with their computers over the course of several weeks. Each participant also performed a set of pointing tasks in a formal study set-up. For each movement, we computed a set of measures capturing nuances of the trajectory and the speed, acceleration, and jerk profiles. Treating the observations from the formal study as positive examples of deliberate, targeted movements and the in situ observations as unlabeled data with an unknown mix of deliberate and distracted interactions, we used a recent advance in machine learning to develop the classifiers. Our results show that, on four distinct metrics, the data collected in-situ and filtered with our classifiers closely matches the results obtained from the formal experiment.