Pointing Endpoint Prediction

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Current Version 1.0.0

Windows executable: KTMExe.zip
Source code: KTM.zip

This software requires the Microsoft .NET Framework 4.0 Client Profile or later. This software is distributed under the New BSD License agreement.

About

We present a new method of predicting the endpoints of mouse movements. While prior approaches to endpoint prediction have relied upon normative kinematic laws, regression, or control theory, our approach is straightforward but kinematically rich. Our key insight is to regard the unfolding velocity profile of a pointing movement as a 2-D stroke gesture and to use template matching to predict the endpoint based on prior observed movements. We call our technique kinematic template matching (KTM), which is simple to implement, user-adaptable, and kinematically expressive. In a study of 17 able-bodied participants evaluated over movement amplitudes ranging from 100-800 pixels, we found KTM to predict endpoints that were within 83 pixels of the true endpoint at 50% of the way through the movement, within 48 pixels at 75%, and within 39 pixels at 90%, using 1000 templates per participant. These accuracies make KTM as successful an approach to endpoint prediction as any prior technique, while being easier to implement and understand than most.

Video

Pointing endpoint prediction on YouTube.

Publication

Pasqual, P.T. and Wobbrock, J.O. (2014). Mouse pointing endpoint prediction using kinematic template matching. Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI '14). Toronto, Ontario (April 26-May 1, 2014). New York: ACM Press, pp. 743-752.

Acknowledgements

We thank James Fogarty, Mayank Goel, and Krzysztof Z. Gajos for early discussions of this work. This work was supported in part by the National Science Foundation under grant IIS-0952786. Any opinions, findings, conclusions or recommendations expressed in this work are those of the authors and do not necessarily reflect those of the National Science Foundation.