School of Education, Xi'an University, Xi'an, Shaanxi 710065, China.
Occup Ther Int. 2022 Aug 23;2022:2210820. doi: 10.1155/2022/2210820. eCollection 2022.
In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and 1-measure reaches 0.67.
近年来，随着城市化进程的加快和义务教育的实施，学生的学习和生活压力不断增加，心理行为问题日益突出。因此，学校将学生心理健康教育作为教学工作的重中之重。有效的表情分类可以帮助心理学研究人员研究心理学和其他学科，并通过分类表情来分析儿童的心理活动和心理状态，从而减少心理行为问题的发生。目前大多数主流方法主要关注文本显式特征的探索和表示模型的优化，很少有工作关注更深层次的语言表达。隐喻作为日常生活中常用的语言表达方式，与个体的情感、认知和心理状态密切相关。本文基于深度神经网络研究儿童笑脸识别。为了获得更好的儿童心理健康问题识别效果，本文尝试使用多源数据，包括消费数据、访问控制数据、网络日志和年级数据，并提出了一种基于多源数据的心理健康问题识别算法。主要研究重点是特征提取，尝试使用一维卷积神经网络（1D-CNN）从在线行为序列中挖掘学生的在线模式，根据学生在自助餐厅的消费数据计算异常分数，并描述学生之间的饮食差异。同时，本文利用心理中心提供的学生心理状态数据作为标签，弥补问卷的不足。本文使用训练集训练五种常见的分类算法，通过验证集进行评估，并选择最佳分类器作为我们的算法，用于识别测试集中有心理健康问题的学生。实验结果表明，精度达到 0.68，召回率达到 0.56，1 测度达到 0.67。
