2013年--3区--先信号处理，再情绪识别的文章

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该研究提出了一种情绪状态分类算法，包括生理特征提取、统计特征选择和机器学习模型。通过预处理心率(HR)信号和小波分析去除皮肤电反应(GSR)噪声，利用HR和GSR的统计及小波特征来解释唤醒度和愉悦度。小波去噪提高了情感状态分类的正确率，揭示了生理响应与情感之间的清晰关系。

2018-12-29 今天只是整理与特征提取有关的内容

结果：本文的特征提取：HR是人工提取的统计特征，GSR是小波分析特征

标题：

Emotional State Classification in Patient–Robot Interaction Using Wavelet Analysis and Statistics-Based Feature Selection

（小波分析、基于统计）

3区期刊 2013年 IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, VOL. 43, NO. 1, JANUARY 2013
作者：Manida Swangnetr and David B. Kaber, Member, IEEE

先简单概述一下本篇文章：

这是目前找到的唯一一篇在进行信号处理之后，再进行情绪识别的文章。

本文研究的目的是开发一种新的计算算法，用于医疗服务期间与护理机器人的交互中准确的患者情绪分类。应用：在医院环境中交互式机器人的实际应用，准确分析患者的情绪。

数据：HR（心率）、GSR（皮肤电反应）

情绪分类二维 valence( happy -- unhappy ) 、arousal ( excited -- bored )

摘要：

A three-stage emotional state classification algorithm was applied to these data, including: 1) physiological feature extraction; 2) statistical-based feature selection; and 3) a machinelearning model of emotional states.

A pre-processed HR signal was used. GSR signals were nonstationary and noisy and were further processed using wavelet analysis. A set of wavelet coefficients, representing GSR features, was used as a basis for current emotional state classification. Arousal and valence were significantly explained by statistical features of the HR signal（HR信号的统计特征） and GSR wavelet features（GSR小波特征）. Wavelet-based de-noising of GSR signals led to an increase in the percentage of correct classifications of emotional states and clearer relationships among the physiological response and arousal and valence.

这段话说的是：本文算法的三个阶段：1）生理特征提取；2)基于统计的特征选择；3)情绪状态的机器学习模型。

采用被预处理后的HR信号。GSR信号是非平稳并且有噪声的，使用小波分析进一步处理。

Prior studies have represented stochastic physiological signals using statistical features (based on expert domain knowledge) to classify emotional states. Unfortunately, information can be lost with such features as simplifying assumptions are made, including knowledge of the probability density function of the data. Furthermore, there may be signal features that have not been identified by experts, but have the potential to significantly improve emotion classification accuracy. It has been suggested that signal processing features may be useful for this purpose [27].

这一段是基于统计提取特征的缺点：先前的研究已经用统计特征(基于专家领域知识)来表示随机生理信号来对情绪状态进行分类。不幸的是，如果进行简化假设，包括对数据的概率密度函数的了解，信息就会丢失。此外，可能有一些信号特征尚未被专家识别，但有可能显著提高情绪分类的准确性。有人建议，信号处理特征可用于这一目的[27]。

另外，文章的C部分，是用于情绪分类的建模方法即： C. Current Modeling Approaches for Classifying Emotional States