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中国医疗器械杂志 2020, Vol. 44 Issue (4) :283-287    DOI: 10.3969/j.issn.1671-7104.2020.04.001
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基于多种生理信号的情绪识别研究
陈沙利1, 2, 3,张柳依4,江锋1, 2, 3,陈婉琳1, 2, 3,缪家骏1, 2, 3,陈杭1, 2, 3
1 浙江大学 生物医学工程与仪器科学学院,杭州市,310027
2 生物医学工程教育部重点实验室,杭州市,310027
3 浙江省心脑血管检测技术与药效评价重点实验室,杭州市,310027
4 浙江大学 心理与行为科学系,杭州市,310027
Emotion Recognition Based on Multiple Physiological Signals
CHEN Shali1, 2, 3, ZHANG Liuyi4, JIANG Feng1, 2, 3, CHEN Wanlin1, 2, 3, MIAO Jiajun1, 2, 3, CHEN Hang1, 2, 3
1 College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, 310027
2 Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310027
3 Zhejiang Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal
Effectiveness Appraisal, Zhejiang University, Hangzhou, 310027
4 Department of Psychology and Behavioral Science, Zhejiang University, Hangzhou, 310027

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摘要 情绪是由某种特定的对象或情景所引发的一系列反应,会影响人的生理状态,因此可通过生理信号进行识 别。提出一种融合脉搏波、皮肤电反应、呼吸、皮肤温度等多种信号的特征,结合SVM-RFE-CBR(基于 支持向量机可减少相关性偏差的递归特征消除)特征排序算法进行特征选择,利用支持向量机进行分类的 情绪识别模型,并通过DEAP数据集验证该模型在愉悦度、唤醒度、优势度上的二分类效果,分别获得了 73.5%、81.3%、76.1%的准确率。结果表明,利用多种生理信号融合可有效地进行情绪识别。
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关键词情绪识别   多生理信号   支持向量机     
Abstract: Emotion is a series of reactions triggered by a specific object or situation that affects a person's physiological state and can, therefore, be identified by physiological signals. This paper proposes an emotion recognition model. Extracted the features of physiological signals such as photoplethysmography, galvanic skin response, respiration amplitude, and skin temperature. The SVM-RFE-CBR(Recursive Feature Elimination-Correlation Bias Reduction-Support Vector Machine) algorithm was performed to select features and support vector machines for classification. Finally, the model was implemented on the DEAP dataset for an emotion recognition experiment. In the rating scale of valence, arousal, and dominance, the accuracy rates of 73.5%, 81.3%, and 76.1% were obtained respectively. The result shows that emotional recognition can be effectively performed by combining a variety of physiological signals.
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Received 2019-11-19;
Corresponding Authors: 陈杭     Email: ch-sun@263.net
About author: 陈沙利,E-mail:21815049@zju.edu.cn
引用本文:   
陈沙利1, 2, 3,张柳依4,江锋1, 2, 3,陈婉琳1, 2, 3,缪家骏1, 2, 3,陈杭1, 2, 3.基于多种生理信号的情绪识别研究[J]  中国医疗器械杂志, 2020,V44(4): 283-287
CHEN Shali1, 2, 3, ZHANG Liuyi4, JIANG Feng1, 2, 3, CHEN Wanlin1, 2, 3, MIAO Jiajun1, 2, 3, CHEN Hang1, 2, 3.Emotion Recognition Based on Multiple Physiological Signals[J]  Chinese Journal of Medical Instrumentation, 2020,V44(4): 283-287
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