SPEED: prédiction de cibles

Authors:
Jonathan Wonner;Jérôme Grosjean;Antonio Capobianco;Dominique Bechmann
Affiliations:
ENS Cachan-Bretagne, Campus de Ker Lann, Bruz, France;LSIIT UMR, Bd Sébastien Brant, Illkirch, France;LSIIT UMR, Bd Sébastien Brant, Illkirch, France;LSIIT UMR, Bd Sébastien Brant, Illkirch, France
Venue:
23rd French Speaking Conference on Human-Computer Interaction
Year:
2011

Citing 10
Cited 1

Extending Fitts' law to two-dimensional tasks

CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Refining Fitts' law models for bivariate pointing

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Pointing at trivariate targets in 3D environments

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Object pointing: a complement to bitmap pointing in GUIs

GI '04 Proceedings of the 2004 Graphics Interface Conference
Predictive interaction using the delphian desktop

Proceedings of the 18th annual ACM symposium on User interface software and technology
Fitts' law and expanding targets: Experimental studies and designs for user interfaces

ACM Transactions on Computer-Human Interaction (TOCHI)
Endpoint prediction using motion kinematics

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Evaluation of pointing performance on screen edges

AVI '08 Proceedings of the working conference on Advanced visual interfaces
Speeding pointing in tiled widgets: understanding the effects of target expansion and misprediction

Proceedings of the 15th international conference on Intelligent user interfaces
Benchmarking pointing techniques with distractors: adding a density factor to Fitts' pointing paradigm

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Towards a model for predicting intention in 3D moving-target selection tasks

EPCE'13 Proceedings of the 10th international conference on Engineering Psychology and Cognitive Ergonomics: understanding human cognition - Volume Part I

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Abstract

We present the SPEED method to predict endpoints, based on analysis of the kinetic characteristics of the pointing gesture. Our model splits the gesture into an acceleration phase and a deceleration phase to precisely detect target. The first phase allows us to identify a velocity peak that marks the beginning of the second phase. This phase is approached with a quadratic model to predict gesture endpoint. A pilot study shows that SPEED predicts a target more precisely than other existing methods, for 1D tasks without distractors.