Scientific and Technical Journal


ISSN Print 2221-3937
ISSN Online 2221-3805
Subject. The method of construction information technology of multivariate Volterra model for oculo-motor apparatus.
Objective. Development of a method for constructing nonparametric dynamic model of an oculo-motor apparatus in the form of Volterra kernels. It based on experimental "input-output" data and takes into account an inertia and nonlinear properties.
The method of investigation. Construction of a multidimensional Volterra model of oculo-motor apparatus is based on the experimental "input-output" data - pupil reaction to a disturbance in the form of a light spot. The response function of oculo-motor apparatus to the disturbance constructs using intelligent processing algorithms of video changing the position of the pupil. Description of the properties the oculo-motor apparatus is made using the most versatile nonlinear nonparametric dynamic models in the form of Volterra series.
Application results. The proposed technology of tracking the pupil behavior available for widespread use in modern applications with an expanded set of personalized features, such as medical and athletic trainers, authorized access to the data, testing of human-machine systems, and more. An important feature of the technology is simple tastes to the hardware that makes it possible its use in the modern mobile devices.
The novelty and the conclusions of the work. It is offered a method for constructing nonparametric dynamic model of an oculo-motor apparatus. It takes into account inertial and nonlinear properties on the basis of experimental "input-output" data.
On the base of experimental data using effective computational algorithms and software for processing data received nonparametric dynamic model of human oculo-motor apparatus.
1. Kepler J. Universität Linz, (2004), Biomechanical Modelling of the Human Eye, Netzwerkfür Forschung, Lehreund Praxіs, Linz, März.
2. Guestrin E.D., and Eizenman M., (2006), General Theory of Remote Gaze Estimation Using the Pupil Center and Corneal Reflections, IEEE Transitions on Biomedical Engineering, Vol. 53, No. 6, June 2006.
3. Kopaeva V.G. Glaznye bolezni. Osnovy oftal'mologii [Ocular Disease. Fundamentals of Ophthalmology], (2012), Moscow, Russian Federation, Medicina, 552 p. (In Russian).
4. Shamshinova A.M., and Volkov V.V. Funktsional'nye metody issledovaniya v oftal'mologii, [Functional Methods of Research in Ophthalmology], (1999), GEOTAR-Madia, 416 p. (In Russian).
5. Jansson D., Medvedev A., Axelson H., and Nyholm D., (2015), Stochastic Anomaly Detection in Eye-tracking Data for Quantification of Motor Symptoms in Parkinson's Disease, Advanced in Experimental Medicine and Biology, Vol. 823, pp. 63 – 82, doi: 10.1007/978-3-319-10984-8_4.
6. Jansson D., and Medvedev A., (2014), Volterra Modeling of the Smooth Pursuit System with Application to Motor Symptoms Characterization in Parkinson's Disease, European Control Conference (ECC), pp. 1856 –1861, doi: 10.1109/ecc.2014.6862207.
7. Baziyan B.H., Chigaleichik L.A., Testenko E.L, and Lachinova D.R. Ispol'zovanie analiza traektorii dvizheniya glaz, golovy i ruki dlya rannei funktsional'noi diagnostiki bolezni Parkinsona [Using the Analysis of the Eye Movements Trajectory, Head and Hands for early Functional Diagnosis of Parkinson's Disease], (2007), Bjulleten Eksperimentalnoj Biologii i Mediciny, Vol. 143, No. 5, pp. 484 – 486 (In Russian).
8. Westwick D.T., (1995), Methods for the Identification of Multiple–Input Nonlinear Systems, Departments of Electrical Engineering and Biomedical Engineering, McGill University, Montreal, Quebec, Canada.
9. Giannakis G.B., and Serpedin E. (2001), A Bibliography on Nonlinear System Identification and its Applications in Signal Processing, Communications and Biomedical Engineering, Signal Processing – EURASIP, Elsevier Science B.V., 81(3), pp. 533 – 580
10. Doyle F.J, Pearson R.K., and Ogunnaike B.A., (2001), Identification and Control Using Volterra Models. Published Springer Technology & Industrial Arts, 314 p.
11. Sidorov D.N., Metody analiza integral'nykh dinamicheskikh modelei: teoriya i prilozheniya [Methods of integrated dynamic models analysis: Theory and Applications], (2013), Irkutsk, Russian Federation, Pub. IGU, 293 p. (In Russian).
12. Masri M.M. Postroenie approksimatsionnoi modeli Vol'terra nelineinoi sistemy s pomoshch'yu mnogostupenchatykh testovykh signalov [Building of Approximation Model Volterra of Nonlinear System using Multi-test Signals], (2014), Mathematical and Computer Modeling Series: Engineering: Coll. Science. Papers, Institute of Cybernetics. V.N. Glushkov NAS of Ukraine, Kamenets-Podolsky National University named Ivan Ogienko, – Kamenets-Podolsky, Ukraine, Kamenets-Podolsky National University named Ivan Ogienko, Issue. 11, pp. 107 – 116 (In Russian).
13. Fomin O.O., Masri M.M., Pavlenko V.D., and Fedorova A.N. Metod i informatsionnaya tekhnologiya postroeniya neparametricheskoi dinamicheskoi modeli glazo-dvigatel'nogo aparata [Method and Information Technology of the oculo-motor Apparatus Nonparametric Dynamic Model Building], (2015), Eastern-European Journal of Eenterprise Technologies, Kharkov, Ukraine, Issue. 11(70), Vol. 4, pp. 38 – 43, doi: 10.15587/1729-4061.2015.41448 (In Russian).
14. Pavlenko V.D., Fomin O.O. Informatsionnaya tekhnologiya model'noi diagnostiki nelineinykh ob"ektov [Information Technology of Model Diagnostics Nonlinear Objects], (2011), Informatics and Mathematical Methods in Simulation, Odessa, Ukraine, No. 1, Vol 1, pp. 57 – 65 (In Russian).

Last download:
8 July 2020

[ © KarelWintersky ] [ All articles ] [ All authors ]
[ © Odessa National Polytechnic University, 2014-2018. Any use of information from the site is possible only under the condition that the source link! ]