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首页> 外文会议>European Conference on Computer Vision(ECCV 2006) Workshop on Human-Computer Interaction(HCI); 20060513; Graz(AT) >Computing Emotion Awareness Through Facial Electromyography
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Computing Emotion Awareness Through Facial Electromyography

机译：通过面部肌电图计算情绪意识

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摘要

To improve human-computer interaction (HCI), computers need to recognize and respond properly to their user's emotional state. This is a fundamental application of affective computing, which relates to, arises from, or deliberately influences emotion. As a first step to a system that recognizes emotions of individual users, this research focuses on how emotional experiences are expressed in six parameters (i.e., mean, absolute deviation, standard deviation, variance, skewness, and kurtosis) of physiological measurements of three elec-tromyography signals: frontalis (EMG1), corrugator supercilii (EMG2), and zy-gomaticus major (EMG3). The 24 participants were asked to watch film scenes of 120 seconds, which they rated afterward. These ratings enabled us to distinguish four categories of emotions: negative, positive, mixed, and neutral. The skewness of the EMG2 and four parameters of EMG3, discriminate between the four emotion categories. This, despite the coarse time windows that were used. Moreover, rapid processing of the signals proved to be possible. This enables tailored HCI facilitated by an emotional awareness of systems.
机译：为了改善人机交互（HCI），计算机需要识别并正确响应用户的情绪状态。这是情感计算的基本应用，它与情感有关，由情感产生或有意影响情感。作为识别个人用户情绪的系统的第一步，本研究着重于如何通过三种电子生理测量的六个参数（即均值，绝对偏差，标准偏差，方差，偏度和峰度）表达情绪体验。 -肌铁蛋白信号：额叶（EMG1），皱眉器（EMG2）和zy-gomaticus major（EMG3）。要求24位参与者观看120秒的电影场景，然后对其进行评分。这些评级使我们能够区分情绪的四个类别：消极，积极，混合和中立。 EMG2的偏度和EMG3的四个参数区分了四个情感类别。尽管使用了粗略的时间窗口，但这仍然可行。此外，事实证明可以对信号进行快速处理。这可以通过对系统的情感意识促进量身定制的HCI。

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