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Analysis of eye tracking movements using innovations generated by a Kalman filter

  • Physiological Measurement
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Abstract

A simple but efficient algorithm has been developed for computer analysis of eye tracking movements. The program separates smooth pursuit and saccadic eye movements. Separation of the two components is achieved using a twostep process of saccade detection. First, an AR model of the velocity of the smooth component is identified and used to determine a Kalman filter. Secondly the innovation sequence generated by this filter allows saccade detection. The precise beginning and end of each saccade are found using a Hinkley algorithm. Examples are given of analysis procedure for eye tracking of a random moving target. The method proved to be highly reliable and could be easily extended to other eye movements such as nystagmus.

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References

  • Allum, J. H. J., Tole, J. R. andWeiss, A. D. (1975) MITNYS-II a digital program for on-line analysis of nystagmus.IEEE Trans.,BME-22, 196–202.

    Google Scholar 

  • Bach, M., Bouis, D. andFisher, B. (1983) An accurate and linear oculometer.J. Neuro-sciences,9, 9–14.

    Google Scholar 

  • Bahill, A. T., Iandolo, M. J. andTroost, B. T. (1980) Smooth pursuit eye movements in response to unpredictable target waveforms.Vision Res.,20, 923–931.

    Article  Google Scholar 

  • Bahill, A. T., Brockenbrough, A. andTroost, B. T. (1981) Variability and development of a normative data base for saccadic eye movements,Invest. Ophthalmol. Vis. Sci,21, 116–125.

    Google Scholar 

  • Baland, J. F., Godaux, E. R. andCheron, G. A. (1987) Algorithms for the analysis of the nystagmic eye movements induced by sinusoidal head rotations.IEEE Trans.,BME-34, 811–816.

    Google Scholar 

  • Balor, R. W., Langhoffer, L., Honrubia, V. andYee, R. D. (1980) On-line analysis of eye movements using a digital computer.Aviat. Space Environ. Med.,51, 563–567.

    Google Scholar 

  • Barnes, G. R. (1982) A procedure for the analysis of nystagmus and other eye movement.,53, 676–682.

    Google Scholar 

  • Bartlett, M. S., (1984) On the theoretical specification of sampling properties of autocorrelated time series,J. Royal Statistical Society, B8-27.

  • Box, G. E. P. andJenkins, G. M. (1976)Time series analysis: forecasting and control. Holden Day, San Francisco.

    MATH  Google Scholar 

  • Eykhoff, P. (1974)System identification. North-Holland, Amsterdam.

    Google Scholar 

  • Gauthier, G. M. andHofferer, J. M. (1976) Eye tracking of self-moved target in the absence of vision.Exp. Brain Res.,26, 121–139.

    Article  Google Scholar 

  • Gauthier, G. M., Vercher, J. L., Mussa-Ivaldi, F. andMarchetti, E. (1988) Oculo-manual tracking of visual targets: control learning, coordination control and coordination model.,73, 127–137.

    Article  Google Scholar 

  • Hinkley, D. V. (1971) Inference about the change point from cumulative sum-tests.Biometrika,58, 509–523.

    Article  MATH  MathSciNet  Google Scholar 

  • Ljung, L. (1987)System identification: theory for the user. Prentice-Hall, Englewood Cliffs.

    MATH  Google Scholar 

  • Matsuoka, K. andHarato, H. (1983) Detection of rapid phases of eye movements using third order derivatives,Japanese J. Ergonomics,19, 147–153.

    Google Scholar 

  • Mehra, R. K. andPeschon, J. (1971) An innovations approach to fault detection and diagnosis in dynamic systems,Automatic,7, 637–640.

    Article  Google Scholar 

  • Stark, L., Wossius, G. andYoung, L. R. (1962) Predictive control of eye tracking movements.IRE Trans. Human Factors Electronics,3, 52–57.

    Article  Google Scholar 

  • Willsky, A.S. andJones, H. L. (1974) A generalized likelihood ratio approach to state estimation in linear systems subject to abrupt changes. Proc. IEEE Conf. Decision Contr., Phoenix Arizona, 846–853.

  • Willsky, A. S. (1976) A survey of design methods for failure detection in dynamic systems,Automatica,12, 601–611.

    Article  MATH  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. Centre de Recherche en Automatique de Nancy, UA CNRS-821, LARA, Université de Nancy 1, Vandoeuvre, France

    D. Sauter & C. Vomscheid

  2. Service Physiologie Environmentale, Institut National de Recherche et de Sécurité, Vandoeuvre, France

    B. J. Martin & N. Di Renzo

Authors
  1. D. Sauter
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  2. B. J. Martin
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  3. N. Di Renzo
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  4. C. Vomscheid
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Sauter, D., Martin, B.J., Di Renzo, N. et al. Analysis of eye tracking movements using innovations generated by a Kalman filter. Med. Biol. Eng. Comput. 29, 63–69 (1991). https://doi.org/10.1007/BF02446297

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  • DOI: https://doi.org/10.1007/BF02446297

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