Eye-tracking study of the perception of folk art on the example of Gzhel and Khokhloma

Introduction. Folk art is the basis of spiritual and moral education. Given that folk art is entering our lives in a new way, the chosen topic is relevant. Theoretical analysis. Gzhel and Khokhloma have accompanied the development of artistic taste over the centuries. It is known about the influence of art on the psycho-emotional state of a person, an indicator of which is oculomotor activity. Eye movements can be used as indicators of the perceptual process. Empirical analysis. The characteristic values of blinking, fixations and saccades were revealed during the perception of images of Gzhel and Khokhloma, taking into account the preference for one or another folk art. It has been established that those who prefer Gzhel use a contemplative strategy of observation, while those who choose Khokhloma use a research one. Also, when considering blue-white images, persons who have chosen Gzhel use a rational way of perception, and when perceiving gold images, they use an emotional one. Participants who preferred Khokhloma showed an inverse ratio of perception methods. Conclusion. When considering folk art, different ways of processing information are actualized among people who prefer Gzhel and Khokhloma. The identified features of oculomotor activity will make it possible to use these types of folk art in art therapy when conducting additional research

Contact-Free Cognitive Load Recognition Based on Eye Movement

The cognitive overload not only affects the physical and mental diseases, but also affects the work efficiency and safety. Hence, the research of measuring cognitive load has been an important part of cognitive load theory. In this paper, we proposed a method to identify the state of cognitive load by using eye movement data in a noncontact manner. We designed a visual experiment to elicit human’s cognitive load as high and low state in two light intense environments and recorded the eye movement data in this whole process. Twelve salient features of the eye movement were selected by using statistic test. Algorithms for processing some features are proposed for increasing the recognition rate. Finally we used the support vector machine (SVM) to classify high and low cognitive load. The experimental results show that the method can achieve 90.25% accuracy in light controlled condition

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective