A MENTAL ILLNESS DETECTION MODEL FOR COLLEGE STUDENTS BASED ON BODY BEHAVIOR AND FACIAL EXPRESSION FEATURES

Mental illnesses such as depression are typically neurologically related psychological disorders that affect people's mood, thinking and behavior. As the number of students who are concerned about their mental health continues to rise, depression has emerged as a mental health concern that has a significant impact on both students' academic performance and overall lives. To identify depression in students at an earlier stage, the purpose of this study was to provide a potentially unique approach. An approach to the detection of mental illnesses that is based on deep learning networks is proposed in this paper. First, facial expression and physical activity data are utilized for detecting depression. Second, the transformer model is utilized to extract the characteristics of the individual's physical behavior, and the multiregional attention network (MRAN) is utilized to extract the characteristics of the individual's emotions. The information that is obtained from the two modalities is complementary. Finally, at the fusion stage, this work applies the classification prediction of depression and nondepression (normal) at the decision level. This is done to ensure that the respective modal properties that were learned by the two channels are preserved in their entirety. We have demonstrated that our strategy is highly effective by performing experimental validation using a dataset that we developed ourselves. It is possible to identify depression in children at an earlier stage with the help of this effective remedy. It is anticipated that the findings of this study will provide an efficient screening tool for depression to educational institutions and organizations that focus on mental health, hence assisting students in receiving essential assistance and intervention at an earlier stage.