{"title":"利用二次Volterra模型在人的心理生理状态诊断研究中的应用","authors":"Vitaliy Pavlenko, Tetiana Shamanina, Vladyslav Chori","doi":"10.20998/2411-0558.2023.01.09","DOIUrl":null,"url":null,"abstract":"It contains the results of the study of the effectiveness of the application of approximation Volterra models of the first and second order of the oculo-motor system in diagnostic studies of the psychophysiological state of a person based on eye-tracking data. Visual stimuli are used as test signals, which are displayed on the monitor screen at different distances from the initial position, which formally corresponds to the action of step signals with different amplitudes at the OMS input. Experimental studies of the \"input-output\" of the OMS were carried out using the Tobii Pro TX300 eyetracker, and the first-order transient functions and the second-order diagonal intersections of the second-order transient functions were determined based on the eye-tracking data. The resulting transient functions are used to form the spaces of diagnostic features. The diagnostic value of all possible combinations of features in pairs according to the indicator of the probability of correct recognition (PCR) was studied. The research results were obtained using Bayesian classifier training in different spaces of the proposed features. A study of the robustness of features according to the PCR indicator was carried out, the combinations of features with the maximum and most stable PCR indicator were selected. Figs.: 13. Tabl.: 5. Refs.: 28 titles.","PeriodicalId":32537,"journal":{"name":"Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta","volume":"40 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using quadratic Volterra model of the oculo-motor system in diagnostic research of psychophysiological state of human\",\"authors\":\"Vitaliy Pavlenko, Tetiana Shamanina, Vladyslav Chori\",\"doi\":\"10.20998/2411-0558.2023.01.09\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"It contains the results of the study of the effectiveness of the application of approximation Volterra models of the first and second order of the oculo-motor system in diagnostic studies of the psychophysiological state of a person based on eye-tracking data. Visual stimuli are used as test signals, which are displayed on the monitor screen at different distances from the initial position, which formally corresponds to the action of step signals with different amplitudes at the OMS input. Experimental studies of the \\\"input-output\\\" of the OMS were carried out using the Tobii Pro TX300 eyetracker, and the first-order transient functions and the second-order diagonal intersections of the second-order transient functions were determined based on the eye-tracking data. The resulting transient functions are used to form the spaces of diagnostic features. The diagnostic value of all possible combinations of features in pairs according to the indicator of the probability of correct recognition (PCR) was studied. The research results were obtained using Bayesian classifier training in different spaces of the proposed features. A study of the robustness of features according to the PCR indicator was carried out, the combinations of features with the maximum and most stable PCR indicator were selected. Figs.: 13. Tabl.: 5. Refs.: 28 titles.\",\"PeriodicalId\":32537,\"journal\":{\"name\":\"Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta\",\"volume\":\"40 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.20998/2411-0558.2023.01.09\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vestnik Irkutskogo gosudarstvennogo tekhnicheskogo universiteta","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.20998/2411-0558.2023.01.09","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
它包含了一阶和二阶眼动系统近似Volterra模型在基于眼动追踪数据的人的心理生理状态诊断研究中的应用有效性的研究结果。视觉刺激作为测试信号,在距离初始位置的不同距离上显示在监视器屏幕上,形式上对应于OMS输入处不同幅度阶跃信号的作用。利用Tobii Pro TX300眼动仪对OMS的“输入-输出”进行了实验研究,并根据眼动数据确定了一阶瞬态函数和二阶瞬态函数的对角交点。得到的瞬态函数用于形成诊断特征的空间。根据正确识别概率(PCR)指标,研究了所有可能的成对特征组合的诊断价值。利用贝叶斯分类器对所提出特征的不同空间进行训练,得到研究结果。根据PCR指标对特征的鲁棒性进行研究,选择最大且最稳定的PCR指标组合特征。无花果。: 13。Tabl。: 5. 参考文献。: 28部。
Using quadratic Volterra model of the oculo-motor system in diagnostic research of psychophysiological state of human
It contains the results of the study of the effectiveness of the application of approximation Volterra models of the first and second order of the oculo-motor system in diagnostic studies of the psychophysiological state of a person based on eye-tracking data. Visual stimuli are used as test signals, which are displayed on the monitor screen at different distances from the initial position, which formally corresponds to the action of step signals with different amplitudes at the OMS input. Experimental studies of the "input-output" of the OMS were carried out using the Tobii Pro TX300 eyetracker, and the first-order transient functions and the second-order diagonal intersections of the second-order transient functions were determined based on the eye-tracking data. The resulting transient functions are used to form the spaces of diagnostic features. The diagnostic value of all possible combinations of features in pairs according to the indicator of the probability of correct recognition (PCR) was studied. The research results were obtained using Bayesian classifier training in different spaces of the proposed features. A study of the robustness of features according to the PCR indicator was carried out, the combinations of features with the maximum and most stable PCR indicator were selected. Figs.: 13. Tabl.: 5. Refs.: 28 titles.