Wang Kunxia, Zhao Zihao, Shen Xueting, Yamauchi Takashi
School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei, China.
Department of Psychological and Brain Sciences, Texas A&M University, College Station, TX, United States.
Front Neurosci. 2023 Jun 2;17:1180407. doi: 10.3389/fnins.2023.1180407. eCollection 2023.
Human emotions vary with temperature factors. However, most studies on emotion recognition based on physiological signals overlook the influence of temperature factors. This article proposes a video induced physiological signal dataset (VEPT) that considers indoor temperature factors to explore the impact of different indoor temperature factors on emotions.
This database contains skin current response (GSR) data obtained from 25 subjects at three different indoor temperatures. We selected 25 video clips and 3 temperatures (hot, comfortable, and cold) as motivational materials. Using SVM, LSTM, and ACRNN classification methods, sentiment classification is performed on data under three indoor temperatures to analyze the impact of different temperatures on sentiment.
The recognition rate of emotion classification under three different indoor temperatures showed that anger and fear had the best recognition effect among the five emotions under hot temperatures, while joy had the worst recognition effect. At a comfortable temperature, joy and calmness have the best recognition effect among the five emotions, while fear and sadness have the worst recognition effect. In cold temperatures, sadness and fear have the best recognition effect among the five emotions, while anger and joy have the worst recognition effect.
This article uses classification to recognize emotions from physiological signals under the three temperatures mentioned above. By comparing the recognition rates of different emotions at three different temperatures, it was found that positive emotions are enhanced at comfortable temperatures, while negative emotions are enhanced at hot and cold temperatures. The experimental results indicate that there is a certain correlation between indoor temperature and physiological emotions.
人类情绪会随温度因素而变化。然而，大多数基于生理信号的情绪识别研究都忽略了温度因素的影响。本文提出了一个考虑室内温度因素的视频诱发生理信号数据集（VEPT），以探究不同室内温度因素对情绪的影响。
该数据库包含从25名受试者在三种不同室内温度下获得的皮肤电流反应（GSR）数据。我们选择了25个视频片段和3种温度（热、舒适和冷）作为激发材料。使用支持向量机（SVM）、长短期记忆网络（LSTM）和注意力循环残差神经网络（ACRNN）分类方法，对三种室内温度下的数据进行情感分类，以分析不同温度对情感的影响。
三种不同室内温度下的情绪分类识别率表明，在高温下，愤怒和恐惧在五种情绪中识别效果最佳，而喜悦的识别效果最差。在舒适温度下，喜悦和平静在五种情绪中识别效果最佳，而恐惧和悲伤的识别效果最差。在低温下，悲伤和恐惧在五种情绪中识别效果最佳，而愤怒和喜悦的识别效果最差。
本文通过分类从上述三种温度下的生理信号中识别情绪。通过比较三种不同温度下不同情绪的识别率，发现积极情绪在舒适温度下增强，而消极情绪在高温和低温下增强。实验结果表明室内温度与生理情绪之间存在一定的相关性。
