其他摘要 | Depression and anxiety are the most common psychological and mental illnesses in the world. They not only seriously affect patients’ quality of life, work and interpersonal relationships, but also are important factors inducing suicide and bring a heavy burden of disease to the country and society. For a long time, depression and anxiety have mainly used subjective methods such as self-rating or others-assessment scales. These methods have been affected by uncertain factors such as the patient's feeling and expression ability, lying and social praise tendencies, and the evaluator's professional training level. In view of the significant differences in gait characteristics between depressed/anxious patients and healthy people, the use of machine learning methods combined with gait characteristics for objective and automatic diagnosis of depression and anxiety has become an effective complement to subjective diagnostic methods. Two-dimensional gait data has the more advantages in the application market such as convenient collection, low cost, and small amount of calculation. In this article, we mainly achieve the automatic recognition of depression and anxiety by using machine learning models based the two-dimensional gait data.The pre-study used two-dimensional gait data to verify its ability to predict age, gender and big five personality. In the prediction of gender, the MLP method was used to achieve the highest classification accuracy rate of 0.789; in the regression prediction of age, the MLP reached a correlation coefficient of 0.58; in the regression prediction of the big five personality, the best correlation coefficients of the five personality dimensions reached were 0.55 (extroverted), 0.52 (pleasant), 0.52 (conscientiousness), 0.56 (nervous) and 0.52 (openness). In the study of depression identification, we established some regression models based on the scores of the PHQ-9 and CES-D scales and the two-dimensional gait data, the best correlation coefficients of these regression models reached are 0.54 and 0.52 which are closed to the strong correlation level of prediction ability. Similarly, in the study of anxiety, we chose the scores of the anxiety scale GAD-7 and T-AI to establish some regression models, the best correlation coefficients of these regression models reached are 0.52 , which reached a prediction level above medium to near strong correlation. At the same time, we grouped the subjects according to gender and age, and used MLP to train different groups separately, and the results were relatively improved compared with not-grouping. Among them, the best PCC of the gender and age group all reached above 0.80, also the data showed that the male group was slightly better than the female group, and the older group over 22 years old was slightly better than younger group under 22 years old, these findings indicate that gender and age have a greater influence on the performance of the gait model. Finally, we used multimodal data which are gait and speech data to perform feature- level fusion and decision-level fusion based on Loss values and PCC coefficients on the MLP model. The fusion effect is slightly improved relative to the performance of single- source features, but not obvious.The research in this paper not only proves that the two-dimensional gait data can effectively identify age, gender and big five personality, but also can effectively detects depression and anxiety. Meanwhile it also verifies the influence of gender and age on the gait model. All these findings will promote the development of related application market and industries. |
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