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Modelling and Data Analysis
2020. Vol. 10, no. 2, 74–92
https://doi.org/10.17759/mda.2020100206
ISSN: 2219-3758 / 2311-9454 (online)

Assessment of Bayesian Ternary Gaze Classification Algorithm (I-BDT)

 
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Abstract

I-BDT eyetracking data ternary classification (fixations, saccades, smooth pursuit) algorithm is investigated. Comparison with well-known Identification / Dispersion Threshold (I-DT) algorithm is held (accuracy, precision, recall, F1 measure). A novel approach for additionally filtering the algorithm output by distance/amplitude, area of convex hull is described.

General Information

Keywords: smooth pursuit, classification, Bayesian decision theory, eye movements

Journal rubric: Software

OpenAlex citations: 2

OpenAlex trends: Gaze Tracking and Assistive Technology, Retinal Imaging and Analysis, Glaucoma and retinal disorders

Information about the work in OpenAlex

Number of citations: 2

Topics

Gaze Tracking and Assistive Technology

This cluster of papers focuses on the use of eye tracking technology in human-computer interaction research, usability evaluation, and assistive technology. It covers topics such as gaze estimation, eye movement analysis, remote gaze estimation, pupil detection, head gesture recognition, and its applications in virtual reality.

Number of works: 45339  |  Total number of citations: 404650

Topic detailsв OpenAlex

Retinal Imaging and Analysis

This cluster of papers focuses on the development and validation of deep learning algorithms for the detection and management of retinal diseases, particularly diabetic retinopathy. It includes topics such as vessel segmentation, optic nerve localization, cardiovascular risk prediction, macular degeneration, and glaucoma detection.

Number of works: 70535  |  Total number of citations: 728990

Topic detailsв OpenAlex

Glaucoma and retinal disorders

This cluster of papers focuses on the global prevalence, treatment, and risk factors associated with glaucoma. It covers topics such as intraocular pressure, optic nerve damage, retinal ganglion cell loss, neurodegeneration, and the use of optical coherence tomography for diagnosis and monitoring. The papers also discuss the impact of ocular hypertension, risk factors for visual field progression, and primary open-angle glaucoma.

Number of works: 153308  |  Total number of citations: 2036714

Topic detailsв OpenAlex

Work details in OpenAlex

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2020100206

Published

For citation: Zherdev, I.Y. (2020). Assessment of Bayesian Ternary Gaze Classification Algorithm (I-BDT). Modelling and Data Analysis, 10(2), 74–92. (In Russ.). https://doi.org/10.17759/mda.2020100206

© Zherdev I.Y., 2020

License: CC BY-NC 4.0

References

  1. Santini T., Fuhl W., Kübler T., et al. Bayesian identification of fixations, saccades, and smooth pursuits. ACM ETRA. Charleston, 2016. pp. 163–170. DOI:10.1145/2857491.2857512
  2. Nyström M., Andersson R., Holmqvist K., et al. The influence of calibration method and eye physiology on eyetracking data quality. Behav. Res. Met. 2013. Vol. 45, no 1, pp. 272–288. DOI:10.3758/s13428–012–0247–4
  3. Hooge I., Holmqvist K., Nyström M. The pupil is faster than the corneal reflection (CR): Are video based pupil-CR eye trackers suitable for studying detailed dynamics of eye movements? Vis. Res. 2016. Vol. 128, pp. 6–18. DOI:10.1016/j.visres.2016.09.002.
  4. Larsson L., Nyström M., Ardö H., et al. Smooth pursuit detection in binocular eye-tracking data with automatic video-based performance evaluation. J. Vis. 2016. Vol. 16, no 15, pp. 20. DOI:10.1167/16.15.20
  5. Startsev M., Agtzidis I., Dorr M. 1D CNN with BLSTM for automated classification of fixations, saccades, and smooth pursuits. Behav. Res. Met. 2019. Vol. 51, pp. 556–572. DOI:10.3758/s13428–018–1144–2
  6. Zemblys R., Niehorster D.C., Komogortsev O., et al. Using machine learning to detect events in eye-tracking data. Behav. Res. Met. 2018. Vol. 50, pp. 160–181. DOI:10.3758/s13428–017–0860–3
  7. Komogortsev O.V., Karpov A. Automated classification and scoring of smooth pursuit eye movements in the presence of fixations and saccades. Behav. Res. Met. 2013. Vol. 45, pp. 203–215. DOI:10.3758/s13428–012–0234–9
  8. Komogortsev O. V, Gobert D. V, Jayarathna S., et al. Standartization of automated analyses of oculomotor fixation and saccadic behaviors. IEEE Trans. Biomed. Eng. 2010. Vol. 57, no 11, pp. 2635–2645. DOI:10.1109/tbme.2010.2057429
  9. Kashyap H.J., Detorakis G., Dutt N., et al. A recurrent neural network based model of predictive smooth pursuit eye movement in primates. IEEE IJCNN. Rio de Janeiro, 2018. pp. 5353–5360. DOI:10.1109/IJCNN.2018.8489652
  10. Xivry J.J.O. de, Coppe S., Blohm G., et al. Kalman Filtering Naturally Accounts for Visually Guided and Predictive Smooth Pursuit Dynamics. J. Neurosci. 2013. Vol. 33, no 44, pp. 17301–17313. DOI:10.1523/JNEUROSCI.2321–13.2013

Information About the Authors

Ivan Y. Zherdev, associated researcher, software developer, Moscow State University of psychology and education, Moscow, Russian Federation, ORCID: https://orcid.org/0000-0001-6810-9297, e-mail: ivan866@mail.ru

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