面向非接触式、非稳定和长时间尺度生理信号的情感状态自动识别研究

项目名称： 面向非接触式、非稳定和长时间尺度生理信号的情感状态自动识别研究

项目编号： No.61502291

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 姜大志

作者单位： 汕头大学

项目金额： 20万元

中文摘要： 基于生理信号的学习者情感状态自动识别，可为教学互动、教学设计以及教学现场评估等提供必要的科学依据，但情感识别实验研究与现实应用之间依然存在明显的价值落差。究其原因是：在真实的课堂教学情境中，学习者生理信号的检测、处理与分析的难度增加，面临手段与方法创新的双重挑战。本研究提出用非接触式的检测方法实时捕获学习者学习过程中的生理信号，探索面向长时间尺度、非稳定复杂生理信号的处理、分析与特征提取方法，进而基于高维、混合型数据构建新型演化算法实现情感状态的自动识别。本研究的意义在于通过发展新型手段与方法从生理信号中高效地分析并收集真实课堂教学情境下学习者的情感状态数据（本次研究的目标），为后续揭示情感状态与学习效果之间的内在影响与规律奠定基础。由于情感识别的应用面极广，该研究的预期成果不仅为人机交互、人际交互提供崭新模式，还可为诸多领域中基于情感识别的创新应用提供极具价值的解决方案。

中文关键词： 演化算法；情感识别；生理信号；非接触式

英文摘要： The recognition of learner’s emotional state with the physiological signals is the necessary and scientific basis for teaching interaction, instructional designed teaching assessment. However, the value drop and distance is still and obviously existed between experimental study and reality application of affective recognition. The reason is: in the realistic classroom instruction, with the increasing difficulty of detection, procession and analysis of learners’ physiological signals, the tools and methods of affective analysis are double challenges which are still worthy of being further discussed, analyzed and explored in terms of the realistic-usability principles. From this point, this research presents an economical, convenient and non-contact measurement to capture the learner’s physiological signals in the learning process, and explores the analysis, procession and feature extraction methods for complex physiological signals. Furthermore, a new type of evolutionary algorithm will be constructed for solving adaptive classification problem based on multiple, high-dimensional and hybrid data. Positioning in realistic classroom instruction, the significance of this research is to develop new tools and methods for analyzing and collecting the learner’s emotional state data from the physiological signals (the goal of this current research) efficiently. Thus, naturally and hopefully, this research is expected to lay a foundation for the future research of the inherent influence and regularity between the emotional state and learning. Since the affective recognition is widely used, the expected results of this research will not only provides a brand new model for human-computer interaction, interpersonal interaction, but also a valuable solution for innovative applications based on emotional state recognition in many and different fields.

英文关键词： evolutionary algorithm;affective recognition ;physiological signal;noncontact