Analyzing the kinematics of bivariate pointingJaime Ruiz, David Tausky, Andrea Bunt, Edward Lank, and Richard Mann
Despite the importance of pointing-device movement to efficiency in interfaces, little is known on how target shape impacts speed, acceleration, and other kinematic properties of motion. In this paper, we examine which kinematic characteristics of motion are impacted by amplitude and directional target constraints in Fitts-style pointing tasks. Our results show that instantaneous speed, acceleration, and jerk are most affected by target constraint. Results also show that the effects of target constraint are concentrated in the first 70% of movement distance. We demonstrate that we can discriminate between the two classes of target constraint using Machine Learning with accuracy greater than chance. Finally, we highlight future work in designing techniques that make use of target constraint to improve pointing efficiency in computer interfaces.
Authors
Jaime Ruiz
As well as: David Tausky, Andrea Bunt, Edward Lank, and Richard Mann
Citation
Jaime Ruiz, David Tausky, Andrea Bunt, Edward Lank, and Richard Mann. 2008. Analyzing the kinematics of bivariate pointing. In Proceedings of Graphics Interface 2008 (GI ’08). Canadian Information Processing Society, CAN, 251–258.
Bibtex
@inproceedings{10.5555/1375714.1375756,
author = {Ruiz, Jaime and Tausky, David and Bunt, Andrea and Lank, Edward and Mann, Richard},
title = {Analyzing the Kinematics of Bivariate Pointing},
year = {2008},
isbn = {9781568814230},
publisher = {Canadian Information Processing Society},
address = {CAN},
abstract = {Despite the importance of pointing-device movement to efficiency in interfaces, little is known on how target shape impacts speed, acceleration, and other kinematic properties of motion. In this paper, we examine which kinematic characteristics of motion are impacted by amplitude and directional target constraints in Fitts-style pointing tasks. Our results show that instantaneous speed, acceleration, and jerk are most affected by target constraint. Results also show that the effects of target constraint are concentrated in the first 70% of movement distance. We demonstrate that we can discriminate between the two classes of target constraint using Machine Learning with accuracy greater than chance. Finally, we highlight future work in designing techniques that make use of target constraint to improve pointing efficiency in computer interfaces.},
booktitle = {Proceedings of Graphics Interface 2008},
pages = {251–258},
numpages = {8},
keywords = {hidden Markov models, maching learning, Fitts' law, bivariate pointing, kinematics},
location = {Windsor, Ontario, Canada},
series = {GI '08}
}