Beyond Fitts' law: models for trajectory-based HCI tasks
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Force-feedback improves performance for steering and combined steering-targeting tasks
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Presence: Teleoperators and Virtual Environments - Special issue: IEEE VR 2003
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Hover widgets: using the tracking state to extend the capabilities of pen-operated devices
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Measuring the difficulty of steering through corners
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Modeling steering within above-the-surface interaction layers
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Interactive 3d drawing for free-form modeling in scientific visualization and art: tools, methodologies, and theoretical foundations
Evaluating Factors that Influence Path Tracing with Passive Haptic Guidance
HAID '09 Proceedings of the 4th International Conference on Haptic and Audio Interaction Design
A Model for Steering with Haptic-Force Guidance
INTERACT '09 Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II
Revisiting path steering for 3D manipulation tasks
International Journal of Human-Computer Studies
Revisiting path steering for 3D manipulation tasks
3DUI '10 Proceedings of the 2010 IEEE Symposium on 3D User Interfaces
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Path steering is an interaction task of how quickly one may navigate through a path. The steering law, proposed by Accot and Zhai [AZ97], is a predictive model which describes the time to accomplish a 2D steering task as a function of the path length and width. In this paper, we study a 3D steering task in the presence of force feedback. Our goal is to extend the application of the steering law in such a task and find out, if possible, additional predictors for users' temporal performance. In particular, we quantitatively examine how the amount of force feedback influences the movement time. We have carried out a repeated-measures-design experiment with varying path length, width and force magnitude. The results indicate that the movement time can be successfully modeled by path length, width and force magnitude. The relationship evidences that the efficiency of the tasks can be improved once an appropriate force magnitude is applied. Additionally, we have compared the capacity of our model to the steering law. According to Akaike Information Criterion (AIC), our model provides a better description for the movement time when the force magnitude can vary. The new model can be utilized as a guideline for designing the experiments with a haptic device.