Issue 2 (200), article 4


Cybernetics and Computer Engineering, 2020, 2(200)

KOCHINA M.L.1, DSc (Biology), Professor,
Head of the Medical and Biological
Basics of Sports and Physical Rehabilitation Department

KOZAK L.M.2, DSc (Biology), Senior Researcher,
Leading Researcher of the Medical Information Systems Department

YAVORSKY O.V.3, DSc (Medicine),
Professor of Ophthalmology Department

FIRSOV O.G.4, PhD (Engineering),
Chief Designer

YEVTUSHENKO A.S.5, PhD (Medicine),

1Petro Mohyla Black Sea National University
10, 68-Desantnykiv st., Mykolaiv, 54000, Ukraine
2International Research and Training Centre for Information Technologies and Systems of the National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine,
40, Acad. Glushkov av., Kyiv, 03187, Ukraine
3Kharkiv National Medical University,
4, Nauky av., Kharkiv, 61000, Ukraine
1, Aviation st., Kharkiv, 61166, Ukraine
5L.L. Hirschman Kharkiv city clinical hospital №14
5, Oles Honchar st., Kharkiv, 61000, Ukraine


Introduction. During mental work, 90% of information is perceived by the human visual system (VS), so the effectiveness of the activities depends on the quality of the VS functioning and the presenting of visual information, especially non-traditional forms (TV, personal computer monitor, miniature displays on mobile phones, e-books). Prolonged information overload can lead to the states such as chronic stress, chronic fatigue syndrome, neurosis, occupational burnout and asthenopia, which worsen the operator` functional state, affect the quality of work tasks performance, last a long time and require special correction and treatment.

The purpose of the paper is to develop a method for evaluating and predicting the operators` functional state based on a model for predicting changes of the VS state under the visual work, as well as to implement this method in clinical decision support system for analyze the SV states changes because of visual work.

Results. Two clusters have been identified according to the mechanisms of changes in the VS state due to visual work. A model for predicting these changes is developed based on a set of indicators of the SV functional state using the fuzzy clustering algorithm (c-means) and the fuzzy derivation system Sugeno. According to results of previous research and this forecast model, a method for assessing and forecasting the functional state of a operator and his visual system has been developed. The proposed method is implemented in clinical decision support system for analysis and prediction of changes of the operator’s VS state due to visual work.

Conclusions. Developed method and automated system allow to predict changes of VS state in the case of a given visual load, to compare the current functional state with the previous one, to obtain information about the effectiveness of the recommended preventive measures. Approbation of the developed system determined that the use of this method of operators` functional state assessment and prediction, as well as recommendations for individual correction of the existing state led to improving of visual function in 67% of patients, and reducing of overall complaints in 50%, visual complaints in 53%, eye complaints – in 40% of patients.

Key words: functional state of visual system, visual load, model for forecasting of VS state, asthenopia, fuzzy clustering, clinical decision support system

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Received 03.01.2020