基于表面肌电的头部运动体态语言情感识别模型  

Surface EMG Based Emotion Recognition Model for Body Language of Head Movements

作　　者：杨钟亮[1] 王健[2] 陈育苗[3] 

机构地区：[1]东华大学机械工程学院,上海201620 [2]浙江大学体育科学与技术研究所,杭州310028 [3]东华大学服装.艺术设计学院,上海200051

出　　处：《计算机辅助设计与图形学学报》2014年第9期1396-1402,共7页Journal of Computer-Aided Design & Computer Graphics

基　　金：国家自然科学基金(51305077);国家"八六三"高技术研究发展计划(2013AA013705);中央高校基本科研业务费专项资金(2232013D3-31)

摘　　要：为了准确识别人机交互中体态语言的情感态度,提出了基于表面肌电的头部运动情感识别模型.针对自发表达"同意"与"不同意"态度的点头与摇头动作招募了8名被试,分别采集颈部头夹肌、胸锁乳突肌和斜方肌的表面肌电信号,通过单因素方差分析提取了这2种情感态度具有显著性差异的10个肌电时域特征向量,将其作为模型的输入变量;再利用Elman神经网络建立点头与摇头的情感识别模型;最后将该模型与基于BP神经网络和支持向量机的情感识别模型进行性能比较.实验结果表明,对测试集中"同意"与"不同意"情感态度的准确识别率超过96%,从而验证了文中模型的可靠性.To recognize emotional attitudes of body language accurately in human-computer interaction, a surface electromyography based emotion recognition model of head movements is proposed. Aiming at analyzing attitudes of agreement and disagreement expressed spontaneously by nodding and shaking head, we recorded the surface electromyographic signals from splenius capitis, sternocleidomastoids and trapeziums of 8 participants. Using one-way domain parameters, with significant differences ANOVA, 10 features of electromyographic time between two attitudes, were extracted as input parameters. The emotion recognition model of head nodding and shaking was constructed using Elman neural network. Finally, the performance of the model was compared with other two emotion recognition models using BP neural network and support vector machine. Experimental results show that correct recognition rates of our model on the test set with agreement and disagreement emotional attitudes are more than 96 %, which demonstrates the reliability of the presented model and method in this paper.

关 键 词：表面肌电 头部运动 体态语言 情感识别 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]