METHODS AND ANALYSIS: This study will adopt a between-group design. Participants will include Malaysian Malays and Caucasian Australians with and without MDD (N=320). There will be four tasks involved in this study, namely: (1) the facial emotion recognition task, (2) the biological motion task, (3) the subjective experience task and (4) the emotion meaning task. It is hypothesised that there will be cultural differences in how participants with and without MDD respond to these emotion tasks and that, pan-culturally, MDD will influence accuracy rates in the facial emotion recognition task and the biological motion task.
ETHICS AND DISSEMINATION: This study is approved by the Universiti Putra Malaysia Research Ethics Committee (JKEUPM) and the Monash University Human Research Ethics Committee (MUHREC). Permission to conduct the study has also been obtained from the National Medical Research Register (NMRR; NMRR-15-2314-26919). On completion of the study, data will be kept by Universiti Putra Malaysia for a specific period of time before they are destroyed. Data will be published in a collective manner in the form of journal articles with no reference to a specific individual.
MATERIALS AND METHODS: We propose a mixed-method study of mental health assessment that combines psychological questionnaires with facial emotion analysis to comprehensively evaluate the mental health of students on a large scale. The Depression Anxiety and Stress Scale-21(DASS-21) is used for the psychological questionnaire. The facial emotion recognition model is implemented by transfer learning based on neural networks, and the model is pre-trained using FER2013 and CFEE datasets. Among them, the FER2013 dataset consists of 48 × 48-pixel face gray images, a total of 35,887 face images. The CFEE dataset contains 950,000 facial images with annotated action units (au). Using a random sampling strategy, we sent online questionnaires to 400 college students and received 374 responses, and the response rate was 93.5%. After pre-processing, 350 results were available, including 187 male and 153 female students. First, the facial emotion data of students were collected in an online questionnaire test. Then, a pre-trained model was used for emotion recognition. Finally, the online psychological questionnaire scores and the facial emotion recognition model scores were collated to give a comprehensive psychological evaluation score.
RESULTS: The experimental results of the facial emotion recognition model proposed to show that its classification results are broadly consistent with the mental health survey results. This model can be used to improve efficiency. In particular, the accuracy of the facial emotion recognition model proposed in this paper is higher than that of the general mental health model, which only uses the traditional single questionnaire. Furthermore, the absolute errors of this study in the three symptoms of depression, anxiety, and stress are lower than other mental health survey results and are only 0.8%, 8.1%, 3.5%, and 1.8%, respectively.
CONCLUSION: The mixed method combining intelligent methods and scales for mental health assessment has high recognition accuracy. Therefore, it can support efficient large-scale screening of students' psychological problems.