首页> 外文会议>International Conference on Neural Interface and Control; 20050526-28; Wuhan(CN) >Modeling and Application of Multimodal Affective User Interface with Multimedia Computer Sensing
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Modeling and Application of Multimodal Affective User Interface with Multimedia Computer Sensing

机译:多媒体计算机感知的多模式情感用户界面建模与应用

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Emotions play an important role in people's everyday life. It's desirable to create intelligent computer systems that understand users' emotional states. In this paper we report on our efforts in developing multimodal affective user interface system used in emotion assessment. First, we discuss the importance of emotion research. Secondly, we introduce the prototype MAUI system built in BABYLAB. There are different modalities of synchronous inputs from the user using wireless sensors and multimedia computer sensing, including physiological signals, facial expressions, speech signals, and behavioral data. The physiological signals are collected using wireless remote sensors in real time. The images of facial expression and the ongoing videos of behavior and gesture are recorded by several cameras connected to different controlling computers. Signals captured simultaneously during experiments are saved in order to discern emotional state from processing sensory modalities over time. All these signals and experiments are collected synchronously in real time to make multimodal signal analysis convenient. Then, using this system, we present preliminary results from an exploratory study that aims to estimate emotion state with non-invasive technologies, which are mapped to their corresponding emotions. Finally, we discuss usages of this system in educational area, and people may benefit from improved satisfaction in learning and training.
机译:情绪在人们的日常生活中起着重要的作用。期望创建能够理解用户情绪状态的智能计算机系统。在本文中,我们报告了我们在开发用于情绪评估的多模式情感用户界面系统方面所做的努力。首先,我们讨论情绪研究的重要性。其次,我们介绍了在BABYLAB中构建的原型MAUI系统。使用无线传感器和多媒体计算机感测的用户有多种同步输入方式,包括生理信号,面部表情,语音信号和行为数据。使用无线远程传感器实时收集生理信号。面部表情以及正在进行的行为和手势视频均由连接到不同控制计算机的几台摄像机记录下来。保存在实验过程中同时捕获的信号,以便随着时间的流逝从处理感觉模态中辨别出情绪状态。所有这些信号和实验均实时同步收集,以方便进行多模式信号分析。然后,使用该系统,我们提出了一项探索性研究的初步结果,该研究旨在利用非侵入性技术估计情绪状态,并将其映射到其相应的情绪。最后,我们讨论了该系统在教育领域的用法,人们可能会从学习和培训的满意度中受益。

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