"悲伤的微笑"曲线:处理来自社交网络的情感信息,以评估热舒适感知
Yifeng Liu1, Xinyue Zhang1, Hongxu Wei1
1State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Journal of thermal biology
|December 17, 2024
概括
使用深度学习分析的面部表情可以预测热舒适度. 一项研究发现,
科学领域:
- 环境科学 环境科学
- 心理学 心理学 心理学
- 计算机科学 计算机科学
背景情况:
- 热舒适度评估传统上依赖于气象因素,由于其主观性质,对精确评估提出了挑战.
- 人类面部表情本质上反映了对不同热环境的反应,为感知舒适提供了潜在的代理.
- 深度学习模型可以分析面部情绪得分,比单纯的物理因素更准确地预测热舒适度.
研究的目的:
- 研究面部情绪得分与感知到的热舒适度之间的相关性.
- 开发一种基于面部表情的深度学习模型来预测基于面部表情的热舒适度.
- 用面部表情分析来确定最佳温度范围以获得热舒适.
主要方法:
- 通过社交网络收集了来自49个城市82个公园的志愿者的8314张面部照片.
- 使用专业仪器分析了快乐,悲伤和中立情绪分数的面部表情.
- 应用了一个U形曲线,适合温度响应的悲伤得分 (SS),以确定舒适温度,并使用ResNet模型进行预测.
主要成果:
- 确定了一个'悲伤的微笑'现象,悲伤得分跟随温度的U形曲线.
- 预测最舒适的温度为22.84°C,舒适度范围为14.62-31.06°C.
- 在预测基于情感的热舒适度方面,ResNet模型取得了高准确性 (R2>0.5),在较冷的季节,临界温度点与SS负相关.
结论:
- 面部表情分析,特别是悲伤得分,提供了一种可靠的方法来评估感知到的热舒适度.
- 深度学习,以ResNet模型为例,有效地预测面部表情的热舒适度.
- 全国范围的地图显示了许多中国城市的舒适度潜力,通过面部表情来评估热量条件.
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