Depression detection system based on Gaze Tracking technology

Wanqing Zhang

doi:10.54097/x4fsdp11

Authors

Wanqing Zhang

DOI:

https://doi.org/10.54097/x4fsdp11

Keywords:

eye tracking, depression, artificial intelligence.

Abstract

Depression is a common mental health problem that affects individuals in many aspects, including emotional, cognitive, behavioral, and physiological levels. The impact of depression is profound, not only affecting the patients themselves but also affecting their families and society. Therefore, timely recognition of the symptoms of depression and seeking professional mental health services are critical to improving patients' quality of life and preventing potentially serious consequences. Based on psychology and computer vision research, this paper proposes a method to detect depression using GazeTracking technology. This method is simple to use, has small requirements for the running environment and cost, and helps detect and prevent depression in multiple scenarios.

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References

World Health Organization official website: who.int

Blue Book of National Depression in 2022. (2022). Beijing: Social Sciences Academic Press

Research progress of eye tracking paradigm in depression evaluation (DOI: 10.3969/j.issn.1009-6574.2020.05.006)

Research progress of Eye tracking paradigm in the evaluation of depression 20200506 Yang Xiaofan Feng Lei Feng Yuan Wang Gang 100088 National Clinical Medical Research Center of Mental Disorders, Beijing Anding Hospital Affiliated to Capital Medical University Beijing Key Laboratory of Diagnosis and Treatment of Mental Disorders DOI: 10.3969 / j.i SSN. 1009-6574.2020.05.006

Almudena D, Carmelo V. Double attention bias for positive and negative emotional faces in clinical depression: Evidence from the an eye - tracking study [J]. J Behav Ther Exp Psychiatry, 46-48 (2015:107-114. The DOI: 10.1016 / j.j btep. 2014.09.005.

Lu SF, Xu JY, Li M, et al. Attentional bias scores in patients with depression and effects of age: A controlled, eye tracking study [J]. Int J Med Res, 2017, (5) : 1518-1527. The DOI: 10.1177/0300060517708920.

Isaac L, Vrijsen JN, Rinck M, et al. Shorter gaze duration for happy faces in current but not remitted depression: Evidence from eye movements [J]. Am J Psychiatry Res, 2014218 (1/2) : 79-86. The DOI: 10.1016 / j. sychres. 2014.04.002.

Xu Xiliang, Liu Mingfan. Eye movement study on emotional face attention bias of individuals in remission of recurrent depression [J]. Chinese Journal of Behavioral Medicine and Brain Sciences, 2015,24 (9) : 824-827. DOI: 10.3760/ cma.j.issn.1674-6554.2015.09.014.Xu XL, Liu MF. Attentional bias to emotional faces on individuals in the remitted phase of recurrent depression: an eyetracking study. Chin J Behav Med & Brain Sci, 2015,24 (9) : 824-827.

Li Wenjing, Li Mi, Lv Shengfu, et al. A study on the eye movement bias of emotional face attention processing in depression remission [J]. Chinese Journal of Neuropsychiatric Disorders, 2015,41 (11) : 698-700. (in Chinese) DOI: 10.3969/j.issn.1002-0152.2015.11.013.

Li M, Lu SF, Wang G, et al. Alleviated negative rather than positive attentional bias in patients with depression in remission: The an eye - tracking study [J]. Int J Med Res, 2016, 44 (5) : 1072-1086. The DOI: 10.1177/0300060516662134.

Pierrot-Deseilligny C, Muri RM, Ploner CJ, et al. Decisional role of the dorsolateral prefrontal cortex in ocular motor behaviour [J]. Brain, 2003,126 (6) : 1460-1473. DOI: 10.1093/brain/awg148.

Curtis CE, Connolly JD. Saccade preparation signals in the human frontal and parietal cortices [J]. J Neurophysiol, 2008, 99 (1) : 133-145. DOI: 10.1152/jn.00899.2007.

Li M, Cao L, Zhai Q, et al. Method of Depression Classification Based on Behavioral and Physiological Signals of Eye Movement. [J]. Complexity, 2020,2020 (1) : 1-9. DOI: 10.1155/2020/4174857.

Zhu J, Wang ZH, Gong T, et al. et al. An Improved Classification Model for Depression Detection Using EEG and Eye Tracking Data [J]. IEEE Trans Nanobioscience, 2020, 12 (3) : 527-537. The DOI: 10.1109 / TNB. 2020.2990690.

Ding XF, Yue XX, Zheng R, et al. Classifying major depression patients and healthy controls using EEG, Eye tracking and galvanic skin response data [J]. J Affect Disord, 2019251:156-161. The DOI: 10.1016 / j.j AD 2019.03.058

https://github.com/antoinelame/GazeTracking