Detecting emotional disorder with eye movement features in sports watching DOI Creative Commons
Qiang Wei, Lin Yang, Xucheng Zhang

и другие.

Frontiers in Neurology, Год журнала: 2025, Номер 16

Опубликована: Апрель 29, 2025

Introduction Digital technologies have significantly advanced the detection of emotional disorders (EmD) in clinical settings. However, their adoption for long-term monitoring remains limited due to reliance on fixed testing formats and active user participation. This study introduces a novel approach utilizing common ball game videos–table tennis–to implicitly capture eye movement trajectories identify EmD through natural viewing behavior. Methods An data collection system was developed using VR glasses display sports videos while recording participants' movements. Based prior research collected data, four primary behaviors were identified, along with 14 associated features. Statistical significance assessed t-tests U-tests, machine learning models employed classification (SVM single-feature analysis decision tree significant features) k-fold validation. The reliability proposed paradigm extracted features evaluated intraclass correlation coefficient (ICC) analysis. Results Significance tests revealed 11 table tennis videos, encompassing exploration, fixation, saccade behaviors, only 3 which served as supplemental stimulus, salient re-testing. GazeEntropy emerged most predictive feature, achieving an accuracy 0.88 p -value 0.0002. A model trained all achieved 0.92 accuracy, 0.80 precision, AUC 0.94. ICC further confirmed high key features, including fixation metrics (average, maximum, standard deviation). Discussion highlights potential video effective identification, particularly focusing two characteristics EmD: curiosity exploration psychomotor function. Additionally, participant preferences content influenced diagnostic performance. We propose that future in-home, psychological conditions can leverage interactions daily digital devices, integrating behavioral seamlessly into everyday life.

Язык: Английский

Detecting emotional disorder with eye movement features in sports watching DOI Creative Commons
Qiang Wei, Lin Yang, Xucheng Zhang

и другие.

Frontiers in Neurology, Год журнала: 2025, Номер 16

Опубликована: Апрель 29, 2025

Introduction Digital technologies have significantly advanced the detection of emotional disorders (EmD) in clinical settings. However, their adoption for long-term monitoring remains limited due to reliance on fixed testing formats and active user participation. This study introduces a novel approach utilizing common ball game videos–table tennis–to implicitly capture eye movement trajectories identify EmD through natural viewing behavior. Methods An data collection system was developed using VR glasses display sports videos while recording participants' movements. Based prior research collected data, four primary behaviors were identified, along with 14 associated features. Statistical significance assessed t-tests U-tests, machine learning models employed classification (SVM single-feature analysis decision tree significant features) k-fold validation. The reliability proposed paradigm extracted features evaluated intraclass correlation coefficient (ICC) analysis. Results Significance tests revealed 11 table tennis videos, encompassing exploration, fixation, saccade behaviors, only 3 which served as supplemental stimulus, salient re-testing. GazeEntropy emerged most predictive feature, achieving an accuracy 0.88 p -value 0.0002. A model trained all achieved 0.92 accuracy, 0.80 precision, AUC 0.94. ICC further confirmed high key features, including fixation metrics (average, maximum, standard deviation). Discussion highlights potential video effective identification, particularly focusing two characteristics EmD: curiosity exploration psychomotor function. Additionally, participant preferences content influenced diagnostic performance. We propose that future in-home, psychological conditions can leverage interactions daily digital devices, integrating behavioral seamlessly into everyday life.

Язык: Английский

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