| Title |
Gaze3DFix: Detecting 3D fixations with an ellipsoidal bounding volume
|
|---|---|
| Published in |
Behavior Research Methods, October 2017
|
| DOI | 10.3758/s13428-017-0969-4 |
| Pubmed ID | |
| Authors |
Sascha Weber, Rebekka S. Schubert, Stefan Vogt, Boris M. Velichkovsky, Sebastian Pannasch |
| Abstract |
Nowadays, the use of eyetracking to determine 2-D gaze positions is common practice, and several approaches to the detection of 2-D fixations exist, but ready-to-use algorithms to determine eye movements in three dimensions are still missing. Here we present a dispersion-based algorithm with an ellipsoidal bounding volume that estimates 3D fixations. Therefore, 3D gaze points are obtained using a vector-based approach and are further processed with our algorithm. To evaluate the accuracy of our method, we performed experimental studies with real and virtual stimuli. We obtained good congruence between stimulus position and both the 3D gaze points and the 3D fixation locations within the tested range of 200-600 mm. The mean deviation of the 3D fixations from the stimulus positions was 17 mm for the real as well as for the virtual stimuli, with larger variances at increasing stimulus distances. The described algorithms are implemented in two dynamic linked libraries (Gaze3D.dll and Fixation3D.dll), and we provide a graphical user interface (Gaze3DFixGUI.exe) that is designed for importing 2-D binocular eyetracking data and calculating both 3D gaze points and 3D fixations using the libraries. The Gaze3DFix toolkit, including both libraries and the graphical user interface, is available as open-source software at https://github.com/applied-cognition-research/Gaze3DFix . |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 19% |
| Student > Ph. D. Student | 5 | 19% |
| Student > Master | 3 | 11% |
| Student > Doctoral Student | 2 | 7% |
| Other | 1 | 4% |
| Other | 5 | 19% |
| Unknown | 6 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Psychology | 8 | 30% |
| Computer Science | 4 | 15% |
| Neuroscience | 2 | 7% |
| Engineering | 2 | 7% |
| Agricultural and Biological Sciences | 1 | 4% |
| Other | 5 | 19% |
| Unknown | 5 | 19% |
Attention Score in Context
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#22,764,772
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#2,100
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Outputs of similar age from Behavior Research Methods
#29
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