| 其他摘要 | Mental health issues are already a global problem, with more than 350 million people worldwide suffering from depression, according to data released by the World Health Organization in 2021. It has previously been reported that depressive and anxiety disorders have become the number one and number six causes of non-fatal health loss globally, respectively, and the prevalence continues to increase each year. Low screening and consultation rates for depression and anxiety are prevalent in countries around the world due to the lack of health and medical resources, and traditional scale screening and one-on-one mental health diagnosis methods have shown limitations and inadequacies, so there is an urgent need to explore new methods and models for depression and anxiety perception and identification.This paper focuses on the automatic depression and anxiety perception technology based on natural gait analysis, which includes the following three aspects.(1) Gait behavior feature extraction technology for depression and anxiety recognition. The study used Patient Health Questionnaire (PHQ-9) and Generalized Anxiety Disorder (GAD-7) to assess the depression and anxiety scores of the subjects, and computer technology to detect and track the two-dimensional coordinates of body joints in the gait videos. Through correlation analysis, it was found that the motion fineness of some body joints was significantly and positively correlated with depression or anxiety, and the correlations were more pronounced in females; by the difference test in movement fineness of joint points on the upper and lower groups of depression or anxiety, it was found that there were significant differences in the motion of some joints of the right upper limb during walking between those with upper and lower scores of depression or anxiety. Based on this, gait temporal behavior features were extracted in the time domain and frequency domain, respectively, to describe the characteristics of gait motion variation of the individuals.(2) Study on the construction techniques of depression and anxiety score assessment models based on natural gait analysis. The cross-validation results showed that the correlation coefficient of the depression identification model was above 0.5, the classification accuracy was 86.4% and the AUC value was 0.754. The correlation coefficients of the anxiety identification model was above 0.4, the classification accuracy was 78.4% and the AUC value was 0.610. The results indicated that the models were valid and had practical values.(3) Study on the construction techniques of depression and anxiety classification models based on natural gait analysis. To meet the needs of identifying high-risk groups in depression and anxiety screening, the upper 27% and lower 27% of the scores were selected from the sample data set, and the machine learning classification algorithms were used to construct the upper and lower groups classification models for depression and anxiety, respectively. The classification precision of the depressed group was 0.71, the recall was 0.83, the F1 score was 0.77, and the AUC value was 0.67. The classification precision, recall and F1 score of the anxious group were 0.83, and the AUC value was 0.79. The results showed that the classification models could well distinguish the depression and anxiety high risk groups from the healthy ones.This paper explores the association between gait behavior and depression or anxiety through a data-driven approach, and initially constructs automatic identification models for depression and anxiety. Compared with traditional scale screening methods, this technique has the following advantages: (1) non-intrusive measurement methods with high ecological validity, no learning effect under repeated administration, and can be used for long-term follow-up monitoring; (2) low equipment cost, labor-saving, easy and convenient to use, time-saving and efficient, and suitable for large-scale primary screening scenarios. Due to the above advantages, the automatic depression and anxiety perception technology based on gait analysis can be used for daily monitoring and auxiliary diagnosis of mental health in primary care institutions, as well as for large-scale primary screening of mental disorders, and is expected to be an effective supplement to traditional diagnostic screening methods and be valuable in improving the screening and treatment rates of mental disorders. |
修改评论