Publication | Closed Access
Modeling and predicting pointing errors in two dimensions
30
Citations
12
References
2011
Year
Unknown Venue
EngineeringBivariate Endpoint Deviation3D Pose EstimationHuman Performance ModelingMotor ControlComputer-aided DesignLocalizationMovement AnalysisKinesiologyMotion CaptureHuman Performance MeasuringSystems EngineeringRobot LearningHuman MotionKinematicsComputational GeometryStatisticsGesture ProcessingHealth SciencesGeometric ModelingMachine VisionRehabilitationComputer ScienceGesture RecognitionEye TrackingError ModelHorizontal MovementHuman Movement
Recently, Wobbrock et al. (2008) derived a predictive model of pointing accuracy to complement Fitts' law's predictive model of pointing speed. However, their model was based on one-dimensional (1-D) horizontal movement, while applications of such a model require two dimensions (2-D). In this paper, the pointing error model is investigated for 2-D pointing in a study of 21 participants performing a time-matching task on the ISO 9241-9 ring-of-circles layout. Results show that the pointing error model holds well in 2-D. If univariate endpoint deviation (SDx) is used, regressing on N=72 observed vs. predicted error rate points yields R2=.953. If bivariate endpoint deviation (SDx,y) is used, regression yields R2=.936. For both univariate and bivariate models, the magnitudes of observed and predicted error rates are comparable.
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