Real-time emotion recognition system with multiple physiological signals
Real-time emotion recognition system with multiple physiological signals
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DOI：
10.1299/jamdsm.2019jamdsm0075
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Jyun-Rong Zhuang;Y. Guan;Hayato Nagayoshi;Keiichi Muramatsu;K. Watanuki;Eiichirou Tanaka
中科院分区：
文献类型：
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作者：
Jyun-Rong Zhuang;Y. Guan;Hayato Nagayoshi;Keiichi Muramatsu;K. Watanuki;Eiichirou Tanaka
情感是一种内在的、主观的体验，在人类生活中扮演着重要的角色。有几种方法可以识别人们的情绪，其中最真实的是使用生理信号，因为它们超出了一个人的控制范围，与人类的情绪密切相关。本研究旨在开发一种基于脑电波、心跳和面部肌肉活动三种生理信号的情绪识别系统。它利用深度神经网络(DNN)和马氏-田口系统(MTS)的T方法对多种生理信号进行处理，进而识别人体的情绪状态。因此，通过DNN在一个二维模型上有效地识别了九种情绪，然后与其他几种算法，如MTS，支持向量机，朴素贝叶斯和K-Means进行了比较，验证了其优越的准确性。此外，虽然T方法只提高了价态分类的准确率，但却得到了不同状态下的情绪强度。此外，在本研究中，所提出的DNN被用于更广泛的应用，以准确地理解人类的情绪状态，而T方法被用于响应不同状态下的情绪强度。最后，开发了一个以DNN为分类器的实时情绪识别系统，该系统可以通过对生理信号进行可靠、客观的情绪分析结果，直接监测人体情绪的变化。因此，该方法可以为服务于日常生活活动的机器人或辅助设备提供有用的治疗效果信息。
Emotion is an internal and subjective experience that plays a significant role in human life. There are several methods of recognizing emotions in people, the most authentic of which is using physiological signals, as they are beyond one’s control and strongly correlated with human emotions. This study aims to develop an emotion recognition system based on three physiological signals, namely, brainwave, heartbeat, and facial muscular activity. It utilizes deep neural network (DNN) and the T method of Mahalanobis-Taguchi system (MTS) to process the multiple physiological signals and further recognize the states of human emotion. As such, nine emotions are effectively recognized on a two-dimensional model through the DNN, then compared against several other algorithms, such as MTS, SVM, Naive Bayes, and K-means, where its superior accuracy is validated. Moreover, although the T method only improves the classification accuracy on the valence state, it rather obtains the intensity of emotion in different states. Furthermore, in this study, the proposed DNN is implemented into a wide range of applications for an accurate understanding of the human emotional states, whereas the T method is utilized to respond to the emotional intensity in different states. Finally, a real-time emotion recognition system is developed with DNN as the classifier; this system can directly monitor the variation of the human emotion through reliable and objective emotion analysis results from the physiological signals. Thus, the method can provide useful treatment effect information for robots or assistive apparatus serving activities of daily living.