Recently proposed novel interaction techniques such as cursor jumping [1] and target expansion for tiled arrangements [13] are predicated on an ability to effectively estimate the endpoint of an input gesture prior to its completion. However, current endpoint estimation techniques lack the precision to make these interaction techniques possible. To address a recognized lack of effective endpoint prediction mechanisms, we propose a new technique for endpoint prediction that applies established laws of motion kinematics in a novel way to the identification of motion endpoint. The technique derives a model of speed over distance that permits extrapolation. We verify our model experimentally using stylus targeting tasks, and demonstrate that our endpoint prediction is almost twice as accurate as the previously tested technique [13] at points more than twice as distant from motion endpoint.

Edward Lank, Yi-Chun Nikko Cheng, and Jaime Ruiz. 2007. Endpoint prediction using motion kinematics. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '07). ACM, New York, NY, USA, 637-646.

 author = {Lank, Edward and Cheng, Yi-Chun Nikko and Ruiz, Jaime},
 title = {Endpoint Prediction Using Motion Kinematics},
 booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
 series = {CHI '07},
 year = {2007},
 isbn = {978-1-59593-593-9},
 location = {San Jose, California, USA},
 pages = {637--646},
 numpages = {10},
 url = {},
 doi = {10.1145/1240624.1240724},
 acmid = {1240724},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Fitts' Law, cursor prediction, kinematics, minimum jerk, motion},