| 基于生理信号的实时情感识别系统设计与实现 |
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| 引用本文： | 刘鑫,钟曼莉,林艳飞,刘志文.基于生理信号的实时情感识别系统设计与实现[J].北京理工大学学报,2019,39(S1):176-180. |
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| 作者姓名： | 刘鑫 钟曼莉 林艳飞 刘志文 |
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| 作者单位： | 北京理工大学 信息与电子学院, 北京 100081,北京理工大学 信息与电子学院, 北京 100081,北京理工大学 信息与电子学院, 北京 100081,北京理工大学 信息与电子学院, 北京 100081 |
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| 基金项目： | 国家自然科学基金资助项目（61601028，61431007）；国家重点研发计划项目（2017YFB1002505） |
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| 摘 要： | 设计并实现基于生理信号的实时情感识别系统.以视频为刺激材料诱发受试者高兴、惊奇、悲伤、愤怒、恐惧、平静6种情感，通过MP160生理信号记录仪采集受试者相应情感下的心电、呼吸、脉搏波、皮肤温度、肌电、皮肤电导6种生理信号，采用PCA和SVM结合的算法实现情感的实时分类.最后，系统对4名在校学生进行了实验，6种情感的平均识别率为70%.
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| 关 键 词： | 生理信号 情感识别 特征提取 分类识别 |
| 收稿时间： | 2018/10/20 0:00:00 |
Design and Implementation of a Real-time Emotion Recognition System Based on Physiological signals |
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| LIU Xin,ZHONG Man-li,LIN Yan-fei and LIU Zhi-wen.Design and Implementation of a Real-time Emotion Recognition System Based on Physiological signals[J].Journal of Beijing Institute of Technology(Natural Science Edition),2019,39(S1):176-180. |
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| Authors: | LIU Xin ZHONG Man-li LIN Yan-fei and LIU Zhi-wen |
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| Institution: | School of Information & Electronics, Beijing Institute of Technology, Beijing 100081, China,School of Information & Electronics, Beijing Institute of Technology, Beijing 100081, China,School of Information & Electronics, Beijing Institute of Technology, Beijing 100081, China and School of Information & Electronics, Beijing Institute of Technology, Beijing 100081, China |
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| Abstract: | In this research, we designed and implemented a real-time emotion recognition system by using physiological signals. The video materials were used to stimulate subjects'' emotions of happiness, surprise, sadness, anger, fear and calmness. The MP160 physiological recorder was used to collect the physiological signals of ECG, myoelectricity, skin conductance, respiration and skin temperature of the subjects under the each emotion condition. After preprocessing, the combined algorithm including PCA and SVM was used to realize real-time classification of emotions. Finally, the system took four students as the subjects of the experiments, and the average recognition rate of the six emotions was 70%. |
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| Keywords: | physiological signals emotion recognition feature extraction classification |
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