基于面部表情识别的大学生心理健康评估模型
1School of Accounting, Guangzhou Huashang College, Guangdong, China.
Scientific reports
|November 26, 2025
概括
这项研究引入了一个深度学习模型,用于使用面部表情识别来评估大学生心理健康. 该模型准确地识别心理状态,为大学心理健康服务提供有效和客观的支持.
科学领域:
- 计算机科学 计算机科学
- 心理学 心理学 心理学
- 人工智能的人工智能
背景情况:
- 大学日益关注的心理健康问题需要客观的评估工具.
- 目前用于识别学生心理状态的方法缺乏速度和客观性.
研究的目的:
- 为大学生开发一个先进的心理健康评估模型.
- 使用人工智能提高心理状态识别的准确性和效率.
主要方法:
- 一个双分支深度学习模型,集成面部表情识别.
- 使用层次的滑动窗口提取动态特征 具有光流的变压器.
- 使用MobileViT与卷积和注意力融合提取静态特征.
主要成果:
- 获得了高精度 (0.945),F1得分 (0.934),和AUC (0.963).
- 证明了快速响应时间 (2.2秒) 和高效训练 (2.6秒).
- 在异常状态识别,训练稳定性和概括方面表现出卓越的表现.
结论:
- 拟议的融合模型有效地补充了大学心理健康预警系统.
- 为基于情感识别的心理干预提供智能技术支持.
- 为高等教育中的客观和有效的心理健康评估提供了一个有前途的解决方案.
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