ASSESSMENT OF PSYCHOPHYSIOLOGICAL STATUS USING A CLOUD COMPUTING PLATFORM

Автор(и)

DOI:

https://doi.org/10.30888/2663-5712.2025-33-01-124

Ключові слова:

psychophysiological status, eye movement system, modeling, identification, eye-tracking, machine learning, cloud computing platform.

Анотація

This paper presents a methodology for identifying the human eye movement system (EMS) using Volterra integral models expressed through first- and second-order transient characteristics. Experimental input-output data were collected with visual stimuli pla

Посилання

Tokushige, S.I., Matsumoto, H., Matsuda, S.I., et al. (2023). Early detection of cognitive decline in Alzheimer’s disease using eye tracking in Frontiers in Aging Neuroscience, 15, Article 1123456.

DOI: 10.3389/fnagi.2023.1123456

Ranjana, J., Rajendran, M. (2025). ADET model: Real time autism detection via eye tracking model using retinal scan images in Technology and Health Care.

DOI: 10.1177/09287329241301678

Solodusha, S., Kokonova, Y., Dudareva, O. (2023). Integral models in the form of Volterra polynomials and continued fractions in the problem of identifying input signals in Mathematics, 11(23), 4724.

DOI: 10.3390/math11234724

Bro, V., Medvedev, A. (2023). Continuous and discrete Volterra-Laguerre models with delay for modeling of smooth pursuit eye movements in IEEE Transactions on Biomedical Engineering, 70(1), pp. 97-104

Doyle, F.J., Pearson, R.K., Ogunnaike, B.A. (2001). Identification and control using Volterra models. London: Springer (Communications and Control Engineering)

Weiss, K., Kolbe, M., Lohmeyer, Q., Meboldt, M. (2023). Measuring teamwork for training in healthcare using eye tracking and pose estimation in Frontiers in Psychology, 14, Article 1169940.

DOI: 10.3389/fpsyg.2023.1169940

Cheng, L., Shen, Y.C., He, Q., Zhang, M.J. (2024). Spying with a pilot’s eye: Using eye tracking to investigate pilots’ attention allocation and workload during helicopter autorotative gliding in Heliyon, 10(16), e26789.

DOI: 10.1016/j.heliyon.2024.e26789

Waisberg, E., Ong, J., Paladugu, P., Kamran, S.A., Zaman, N., Lee, A.G., Tavakkoli, A. (2023). Dynamic visual acuity as a biometric for astronaut performance and safety in Life Sciences in Space Research, 37, pp. 3-6.

DOI: 10.1016/j.lssr.2023.01.002

Pavlenko, V., Milosz, M., Dzienkowski, M. (2020). Identification of the oculo-motor system based on the Volterra model using eye tracking technology in Journal of Physics: Conference Series, 1603, 012011.

DOI: 10.1088/1742-6596/1603/1/012011

Pavlenko, V., Ilutsa, A., Kravchenko, Y. (2024). Eye-tracker signals processing in system identification of human oculomotor apparatus with using cloud technologies in WSEAS Transactions on Signal Processing, 20, pp. 125-137.

DOI: 10.37394/232014.2024.20.13

Pavlenko, V., Pavlenko, S. (2018). Deterministic identification methods for nonlinear dynamical systems based on the Volterra model [Deterministic Identification Methods for Nonlinear Dynamical Systems Based on the Volterra Model] in Applied Aspects of Information Technology, 1(1), pp. 9-29.

DOI: 10.15276/aait.01.2018.1

Pavlenko, V., Shamanina, T., Chori, V. (2023). Eye-tracking technology and its application in neuroscience in Proceedings of the 12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Dortmund, Germany, 1, pp. 187-193.

DOI: 10.1109/IDAACS58523.2023.10348754

Pavlenko, V., Lukashuk, D. (2024). Analysis of the accuracy of simulating the human eye movement system based on Volterra models in CEUR Workshop Proceedings of the 11th International Conference “Information Technology and Implementation” (IT&I), Kyiv, Ukraine, pp. 455-465.

URL: https://ceur-ws.org/Vol-3909/Paper_36.pdf

Dautov, Ç.P., Özerdem, M.S. (2018). Wavelet transform and signal denoising using wavelet method in 2018 26th Signal Processing and Communications Applications Conference (SIU), IEEE, pp. 1-4.

DOI: 10.1109/SIU.2018.8404418

Cortes, C., Vapnik, V. (1995). Support-vector networks in Machine Learning, 20(3), pp. 273-297.

DOI: 10.1007/BF00994018

Опубліковано

2025-09-30

Як цитувати

Павленко, В., Лукашук, Д., & Ілуца, А. (2025). ASSESSMENT OF PSYCHOPHYSIOLOGICAL STATUS USING A CLOUD COMPUTING PLATFORM. SWorldJournal, 1(33-01), 196–208. https://doi.org/10.30888/2663-5712.2025-33-01-124

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