ABSTRACT
Visual behaviorally operant method is one of the main detections for identifying animal models of visual diseases, which is mainly through the optomotor response (OMR) and optokinetic reflex (OKR) stimulated by the virtual operating system (VOS). The automated VOS was commonly used as a powerful tool to control the contrast sensitivity and measure the spatial frequency of the monitoring device by adjusting parameters such as grating fringe width, rotation velocity and light intensity, and also to track the OKR, OMR, and the combined movement of OKR and OMR.Both the optimized measuring methods and evaluation indicators including the search coils, the corneal labeling, OMR-arena system, the OMR index, the staircase protocol tests and the improved stimuli from two-dimensional to three-dimensional helped to ensure the validity of test data.Moreover, the introduction of image recognition technology benefited in extracting the body and head contours of mice.Computer algorithms such as deep learning were also applied to analyze and process the visual behavior of diseased mice, which promoted sensitivity, shortened testing time, reduced detection errors and improved data accuracy.For all the factors mentioned, the VOS could be used as an effective research tool for glaucoma, cataract, retinopathy, hereditary eye disease, optic nerve degeneration and others.This article reviewed the value of VOS for visual behavioral assessment in mice models of visual disease from the visual detection methods and assessment indicators.
ABSTRACT
Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. With the rapid development of computer vision, visual behavior analysis aided diagnosis of ASD has got more and more attention. This paper reviews the research on visual behavior analysis aided diagnosis of ASD. First, the core symptoms and clinical diagnostic criteria of ASD are introduced briefly. Secondly, according to clinical diagnostic criteria, the interaction scenes are classified and introduced. Then, the existing relevant datasets are discussed. Finally, we analyze and compare the advantages and disadvantages of visual behavior analysis aided diagnosis methods for ASD in different interactive scenarios. The challenges in this research field are summarized and the prospects of related research are presented to promote the clinical application of visual behavior analysis in ASD diagnosis.
Subject(s)
Humans , Autism Spectrum Disorder/diagnosis , Vision, Ocular , BehaviorABSTRACT
Vision is an important way for human beings to interact with the outside world and obtain information. In order to research human visual behavior under different conditions, this paper uses a Gaussian mixture-hidden Markov model (GMM-HMM) to model the scanpath, and proposes a new model optimization method, time-shifting segmentation (TSS). The TSS method can highlight the characteristics of the time dimension in the scanpath, improve the pattern recognition results, and enhance the stability of the model. In this paper, a linear discriminant analysis (LDA) method is used for multi-dimensional feature pattern recognition to evaluates the rationality and the accuracy of the proposed model. Four sets of comparative trials were carried out for the model evaluation. The first group applied the GMM-HMM to model the scanpath, and the average accuracy of the classification could reach 0.507, which is greater than the opportunity probability of three classification (0.333). The second set of trial applied TSS method, and the mean accuracy of classification was raised to 0.610. The third group combined GMM-HMM with TSS method, and the mean accuracy of classification reached 0.602, which was more stable than the second model. Finally, comparing the model analysis results with the saccade amplitude (SA) characteristics analysis results, the modeling analysis method is much better than the basic information analysis method. Via analyzing the characteristics of three types of tasks, the results show that the free viewing task have higher specificity value and a higher sensitivity to the cued object search task. In summary, the application of GMM-HMM model has a good performance in scanpath pattern recognition, and the introduction of TSS method can enhance the difference of scanpath characteristics. Especially for the recognition of the scanpath of search-type tasks, the model has better advantages. And it also provides a new solution for a single state eye movement sequence.
Subject(s)
Humans , Algorithms , Discriminant Analysis , Eye Movements , Markov Chains , Normal Distribution , ProbabilityABSTRACT
Objetiva-se verificar o número e duração das fixações visuais e qualidade da tomada de decisão de atletas de voleibol das categorias Infanto e Juvenil na análise de cenas reais de jogos. Participaram 25 atletas voluntários do sexo masculino, sendo 11 da categoria Infanto (idade média de 16±0,3 anos; experiência na modalidade de 3,2±1,3 anos) e 14 da categoria Juvenil (idade média de 17,6±0,9 anos; experiência na modalidade de 4±0,9 anos). Utilizou-se para o rastreamento ocular e análise do número e duração das fixações visuais o EyeTracking SMI RED500® fixo durante o teste de avaliação de cenas de jogos de voleibol. Para análise da qualidade da tomada de decisão (TD), avaliou-se a primeira resposta que o voluntário verbalizava como a melhor solução para a ação apresentada na cena. Analisaram-se os pressupostos de normalidade (teste de Shapiro-Wilk), homocedasticidade das variâncias (teste de Levene) e esfericidade (teste de Mauchly), os quais não apresentaram valores significativos. Seguiu-se a realização de uma Análise de Variância - ANOVA One-way (fator: categoria; níveis: 2 Infanto e Juvenil), mantendo-se um nível de significância de 5%. Calculou-se ainda o tamanho do efeito (h² parcial) e poder observado (beta). Não foram observadas diferenças entre atletas das categorias infanto e juvenil para as variáveis "número de fixações visuais" (p=0,010), "duração das fixações visuais" (p=0,642) e "qualidade da TD" (p=0,281). Além disso, reportou-se tamanho do efeito pequeno para todas variáveis analisadas. Considera-se que os atletas já sabem onde fixar o olhar, realizando fixações em locais específicos que apresentam qualidade dos sinais relevantes para uma ótima TD, onde pode-se observar que, em se tratando da comparação entre atletas, a qualidade dos sinais relevantes é mais importante do que a quantidade e duração das fixações visuais...(AU)
This study aims to verify the quantity and the duration of visual fixation as well as the quality of decision-making in volleyball athletes from Infanto and Juveline categories on the analysis of real games scenes. Twenty five male athletics volunteered on this study, where 11 were from the Infanto category (16±0,3 years old; 3,2±1,3 years of experience) and fourteen were from the Juvenile category (17,6±0,9 years old; 17,6±0,9 years of experience). An eye tracker (Eye Tracking SMI RED500®) was used for tracking the ocular and to analyze the quantity and duration of visual fixations during an evaluation of volleyball games scenes. The first answer the volunteer verbalized as the best option for the showed scene was used for analyzing the quality of the decision-making (DM).The data normality, the homoscedasticity of variances and the sphericity was performed by Shapiro-Wilk, Levene and Mauchly tests, respectively, with no significate values. The Analysis of Variance ANOVA One-Way (factor: category; levels: 2 Infanto and Juvenile) was performed with the significant level in 5%. The effect size (h²) and the observed power (beta) were also calculated. No differences in the variables quantity of visual fixation (p=0,010), duration of visual fixation (p= 0,642) and quality of DM (p=0,281) were found between the athletes from the Infanto and Juvenile categories. Moreover, the effect size was reported for all variables analyzed. The athletes already know where to fix the stare by gazing in specific locations that present quality of relevant signals for a great DM, where can be noted the quality of relevant signals is more important than the quantity and duration of visual fixation...(AU)