Graduate School, Xuzhou Medical University, Xuzhou 221004, China.
Institute of Medical Information Security, Xuzhou Medical University, Xuzhou 221004, China.
Int J Environ Res Public Health. 2022 Nov 14;19(22):14976. doi: 10.3390/ijerph192214976.
Mental health assessments that combine patients' facial expressions and behaviors have been proven effective, but screening large-scale student populations for mental health problems is time-consuming and labor-intensive. This study aims to provide an efficient and accurate intelligent method for further psychological diagnosis and treatment, which combines artificial intelligence technologies to assist in evaluating the mental health problems of college students.
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.
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.
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.
已经证明，结合患者面部表情和行为的心理健康评估是有效的，但对大规模的学生群体进行心理健康筛查既费时又费力。本研究旨在提供一种高效准确的智能方法，以进一步进行心理诊断和治疗，结合人工智能技术来辅助评估大学生的心理健康问题。
我们提出了一种结合心理问卷和面部情绪分析的心理健康评估混合方法研究，以大规模全面评估学生的心理健康状况。使用抑郁、焦虑和压力量表 21 版（DASS-21）进行心理问卷。基于神经网络的迁移学习实现面部情绪识别模型，使用 FER2013 和 CFEE 数据集对模型进行预训练。其中，FER2013 数据集由 48×48 像素的人脸灰度图像组成，共包含 35887 张人脸图像。CFEE 数据集包含标注了动作单元（au）的 95 万张人脸图像。我们采用随机抽样策略向 400 名大学生发送在线问卷，收到 374 份回复，回复率为 93.5%。经过预处理，得到 350 份有效结果，其中包括 187 名男性和 153 名女性学生。首先，在在线问卷测试中收集学生的面部情绪数据。然后，使用预训练模型进行情绪识别。最后，整理在线心理问卷得分和面部情绪识别模型得分，给出综合心理评估得分。
提出的面部情绪识别模型的实验结果表明，其分类结果与心理健康调查结果基本一致。该模型可以提高效率。特别是，本文提出的面部情绪识别模型的准确性高于仅使用传统单一问卷的一般心理健康模型。此外，本研究在抑郁、焦虑和压力三个症状方面的绝对误差低于其他心理健康调查结果，分别仅为 0.8%、8.1%、3.5%和 1.8%。
结合智能方法和量表的心理健康评估混合方法具有较高的识别准确率。因此，它可以支持高效地大规模筛选学生的心理问题。
