Eye Tracking Analytics for Mental States Assessment – A Review

Li, Fan; Xu, Gangyan; Feng, Shanshan

doi:10.1109/smc52423.2021.9658674

Cited by 4 publications

(2 citation statements)

References 85 publications

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“…Researchers have found that the mental stress is correlated with pulse rate and pupil dilation when individuals are exposed to virtual environment [30]. Mental stress has been successfully detected by researchers from different eye tracking data such as gaze-bin and entropy by using machine learning, deep learning techniques [31].…”

Section: Researchers Have Successfully Detected the Affective States ...mentioning

confidence: 99%

Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population

Moharana,

Das,

Routray

2024

Int. J. Onl. Eng.

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Stress is a state of mental tension, which helps us to cope with challenges in our life. It makes us progressive when it is positive, but excessive negative stress that perseveres for a long time leads to a state of depressiveness. Longer stressed stage of a human being changes the size, functionality and frequency of response of many internal and external body parameters. By applying computer vision techniques, these changes of body parameters can be tracked to get useful information about the mental stress for a stress affected person. Many studies show the pupil diameter varies significantly with the effect of stress. Our work is based on the study of variation of pupil diameters of stress affected and not affected university students. With the application of different supervised machine learning algorithms, we have observed that the pupil dilates more in case of stress affected students than non-stressed students. We have also found that the pupils of the students dilates more when they were in positive emotional states than their negative emotional states. This work will be helpful for researchers who are working in the field of emotion detection and recognition and affective disorder analysis.

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Section: Researchers Have Successfully Detected the Affective States ...mentioning

confidence: 99%

Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population

Moharana,

Das,

Routray

2024

Int. J. Onl. Eng.

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“…The use of machine learning and statistical analysis applied to physiological data has shown potential in monitoring mental health and assessing human mental states [20]. Machine learning models can be trained on labeled eye-tracking data to classify the user's mental state [21], [22]. However, more investigation is required to develop the ideal eye-tracking paradigms and machine-learning algorithms for correctly diagnosing people with Autism Spectrum Disorder (ASD) and other neurological and neuropsychiatric illnesses.…”

Section: B Model Selectionmentioning

confidence: 99%

PredictEYE: Personalized Time Series Model for Mental State Prediction Using Eye Tracking

Jyotsna,

Amudha,

Ram

et al. 2023

IEEE Access

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Mental health is vital for emotional, psychological, and social well-being. Mental illness can affect thoughts, feelings, and behaviors. Early intervention and specialized care can manage major mental illnesses. Predicting mental state accurately can facilitate behavioral changes and promote overall wellbeing. The paper proposes a novel personalized time series model called PredictEYE, which aims to predict a person's mental state and identify the specific scene responsible for that mental state. The model achieves this by analyzing individuals' eye-tracking time series data while watching calm and stressful videos. The model utilizes deep learning time-series univariate regression model based on Long Short-Term Memory for predicting the future sequence of each feature and a machine learning-based Random Forest algorithm for the mental state prediction. The model's performance was compared across the state-of-the-art literature survey.The predictEYE model could achieve an accuracy of 86.4% accuracy in predicting mental state. Tailoring eye tracking models to individual differences is more effective in comprehending mental states than models that make comparisons across multiple participants, given eye tracking data's unique and distinctive idiosyncratic nature. The eye tracking features play a crucial role in predicting the mental state, and the model is adaptable to work with webcam-based eye tracking and can relate to applications where continuous and non-invasive monitoring is required.

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A Systematic Review on the Visualization of Avatars and Agents in AR & VR displayed using Head-Mounted Displays

Weidner¹,

Boettcher²,

Arboleda

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Eye Tracking Analytics for Mental States Assessment – A Review

Cited by 4 publications

References 85 publications

Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population

Application of Computer Vision Techniques to Study the Relationship between Mental Stress and Pupil Diameter among Student Population

PredictEYE: Personalized Time Series Model for Mental State Prediction Using Eye Tracking

A Systematic Review on the Visualization of Avatars and Agents in AR & VR displayed using Head-Mounted Displays

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