Psychological test of patients with mental disorders based on eye movement data fusion algorithm

Xiang, Zixian; Gao, Wenbin; Tao, Ting; Wang, Ligang; Fan, Chunlei; Xu, Lirui

doi:10.1145/3482632.3487447

Cited by 2 publications

(4 citation statements)

References 6 publications

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“…erefore, basketball players need to have a strong psychological quality to cope with any changes on the court, so that it is not affected [8]. In a large number of practice matches, basketball players not only consume a lot of physical energy, but also consume a lot of psychological ability in their competitive activities [9].…”

Section: Introductionmentioning

confidence: 99%

Psychological Analysis of Athletes during Basketball Games from the Perspective of Deep Learning

Meng

2022

Mobile Information Systems

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Due to the influence of psychological factors during sports, basketball players often change the shooting rhythm in sports, which will reduce the shooting rate and directly affect the training effect. Therefore, we need to analyze the basketball shooting process of some athletes, which is not a complete negation of traditional training methods, but a psychological analysis of some athletes’ shooting in the course of competition, so as to complement the feedback and training information after the game. With the great improvement of computer computing power, deep learning natural language processing can help people analyze and solve previously unsolvable problems in production and life. The psychological emotion of adolescence has a great influence on the study and life of middle school students. At present, the unified monitoring and analysis of the daily life of middle school students need not only a lot of manpower, but also slow speed. If the psychological problems are not found in time and feedback is given, it will cause a series of adverse effects on individual athletes. The neural network model based on deep learning can process students' daily mass text information quickly and accurately, and then give comprehensive judgment, which is a good solution. This paper applies the neural network algorithm of the Bi-LSTM model and CNN model to study the text data, and finally has 95.55% and 90.03% accuracy in the psychological analysis experiment, which provides a feasible solution to solve the batch rapid analysis of psychological changes reflected in the daily text of athletes during basketball. Some suggestions are put forward on how to strengthen and improve the psychological quality of college basketball players and their ability to bear pressure and difficulties.

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Section: Introductionmentioning

confidence: 99%

Psychological Analysis of Athletes during Basketball Games from the Perspective of Deep Learning

Meng

2022

Mobile Information Systems

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“…A detailed survey is performed to understand how statistical methods [73], [74], machine learning [20]- [25], [75], deep learning [16], [20], [26]- [29], and time series models [31], [76]- [79] are applied to physiological signals to determine a person's mental health.…”

Section: B Model Selectionmentioning

confidence: 99%

“…One study used CNN-LSTM algorithms to analyze visual metrics time-series data and accurately classified individuals' mental health metrics levels with high accuracy, highlighting the potential benefits of home-based mental health monitoring for patients after oncologic surgery [16]. The other study proposed the EYE-CNN-DLSTM algorithm for psychological testing based on eye movement tracking data, which uses a fusion strategy combining CNN and DLSTM to evaluate patients with mental disorders [26].…”

Section: B Model Selectionmentioning

confidence: 99%

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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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Psychological test of patients with mental disorders based on eye movement data fusion algorithm

Cited by 2 publications

References 6 publications

Psychological Analysis of Athletes during Basketball Games from the Perspective of Deep Learning

Psychological Analysis of Athletes during Basketball Games from the Perspective of Deep Learning

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

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