Two-Dimensional Emotion Evaluation with Multiple Physiological Signals
Two-Dimensional Emotion Evaluation with Multiple Physiological Signals
复制标题
多种生理信号的二维情绪评估
DOI：
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Eiichirou Tanaka
中科院分区：
文献类型：
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作者：
Jyun;Y. Guan;Hayato Nagayoshi;L. Yuge;Hee;Eiichirou Tanaka
随着机器人在日常生活活动中的作用日益扩大，人们越来越关注人机交互问题。情绪识别一直被认为是人类心理方面的一个重要问题。我们正在开发一种辅助行走装置，它考虑了使用者身体辅助和精神状况之间的相关性。为了将辅助设备与用户的心理状态连接起来，需要实时评估用户的情绪。本研究旨在建立一种基于多重生理信号的二维价-觉醒模型情绪评价新方法。我们基于规范的情感刺激数据库，引出用户的情绪变化，并进一步提取受试者的多种生理信号。此外，我们实现了各种算法（k-means, MTS （Mahalanobis Taguchi System）的T方法和DNN（深度神经网络））来从生理数据中确定情绪状态。最后，研究结果表明，深度神经网络方法可以准确地识别人类的情绪状态。
Extended roles of robots for activities of daily living (ADL) lead to researchers’ increasing attention to human-robot interaction. Emotional recognition has been regarded as an important issue from the human mental aspect. We are developing an assistive walking device which considers the correlation between physical assistance and mental conditions for the user. To connect the assistive device and user mental conditions, it is necessary to evaluate emotion in real-time. This study aims to develop a new method of two-dimensional valence-arousal model emotion evaluation with multiple physiological signals. We elicit users’ emotion change based on normative affective stimuli database, and further extract multiple physiological signals from the subjects. Moreover, we implement various algorithms (k-means, T method of MTS (Mahalanobis Taguchi System) and DNN (deep neural network)) for determining the emotional state from physiological data. Finally, the findings indicate that deep neural network method can precisely recognize the human emotional state.
DOI：
10.1016/0005-7916(94)90063-9
发表时间：
1994-03-01
影响因子：
1.8
作者：
BRADLEY, MM;LANG, PJ
通讯作者：
LANG, PJ