AUTHORS: Vitaliy Pavlenko, Dmytro Salata, Yuri Maksymenko
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ABSTRACT: The paper is devoted to the development of information technology for the construction nonparametric dynamic model instrumental means of oculomotor system (OMS) based on the data video recording of experimental studies 'input-output' (Eye-tracking technology). A mathematical apparatus of the Volterra series is used, which enables taking into account nonlinear and dynamic properties of research objects. Based on the experimental data with the use of test step signals, a nonparametric dynamic model of human eye-movement apparatus was constructed in the form of transitive functions of the 1st, 2nd and 3rd order.
KEYWORDS: Oculo-motor system, modeling, identification, nonlinear dynamic model, Volterra kernels, multidimensional transient functions, Eye-tracking technology
REFERENCES:
[1] J. Kepler and U. Linz, Biomechanical Modelling of the Human Eye, Netz Werk für Forschung, Lehre und Praxіs, Linz, 2004.
[2] E. D. Guestrin and M. Eizenman, General Theory of Remote Gaze Estimation Using the Pupil Center and Corneal Reflections, IEEE Trans. Biomed. Eng., 53 (6), 2006, pp. 1124- 1133. DOI:10.1109/tbme.2005. 863952.
[3] O. V. Komogortsev and A. Karpov, Automated Classification and Scoring of Smooth Pursuit Eye Movements in Presence of Fixations and Saccades, Journal of Behavioral Research Methods, 45 (1), 2013, pp. 1–13.
[4] D. Jansson and A. Medvedev, Volterra Modeling of the Smooth Pursuit System with Application to Motor Symptoms Characterization in Parkinson's Disease, European Control Conference (ECC),2014, pp. 1856-1861. DOI:10.1109/ecc.2014.6862207
[5] D. Jansson, Stochastic Anomaly Detection in Eye-Tracking Data for Quantification of Motor Symptoms in Parkinson's Disease, Advances in Experimental Medicine and Biology,823, 2015, pp. 63-82. DOI:10.1007/978-3-319-10984-8_4
[6] D. Jansson and A. Medvedev, System identification of Wiener systems via VolterraLaguerre models: Application to human smooth pursuit analysis, European Control Conference (ECC), 2015, pp. 2700-2705. DOI: 10.1109 /ECC.2015.7330946
[7] P. Z. Marmarelis and V. Z. Marmarelis, Analysis of Physiological Systems. The White Noise Approach, Plenum Press, New York, 1978.
[8] F. J. Doyle, R. K. Pearson and B. A Ogunnaike, Identification and Control using Volterra Models, Published Springer Technology & Industrial Arts, 2001.
[9] A. Fomin, M. Masri, V. Pavlenko and A. Fedorova, Method and information technology for constructing a nonparametric dynamic model of the oculomotor system, Eastern European Journal of Enterprise Technologies, 2/9 (74), 2005, pp. 64-69. DOI: 10.15587/1729-4061.2015.41448
[10] V. D. Pavlenko, O. O. Fomin, A. N. Fedorova and M. M. Dombrovskyi, Identification of Human Eye-Motor System Base on Volterra Model, Herald of the National Technical University “KhPI”. Subject issue: Information Science and Modelling, Kharkov, NTU “KhPI”, 21 (1193), 2016, pp. 74-85.
[11] G. A. Shekhovtsov, R. P. Shekhovtsova, E. V. Popov and Yu. N. Raskatkin, Calibration of a Digital Photocamera to Measure Distances, Privolzhsky Scientific Journal, 4 (36), 2015, pp.131-140.
[12] P. Viola and M. Jones, Robust Real-Time Object Detection, Second International Workshop On Statistical and Computational Theories of Vision – Modeling, Learning, Computing and Sampling, Vancouver, Canada, July13, 2001, pp. 1-25.
[13] S. V. Viraktamath, M. Katti, A. Khatawkar and P. Kulkarni, Face Detection and Tracking using OpenCV, The SIJ Transactions on Computer Networks & Communication Engineering (CNCE), Vol. 1, No. 3, July-August, 2013, pp. 45-50.
