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Computational Models of Stress in Reading Using Physiological and Physical Sensor Data

机译：使用生理和物理传感器数据进行阅读时的压力计算模型

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摘要

Stress is a major problem facing our world today and it is important to develop an objective understanding of how average individuals respond to stress in a typical activity like reading. The aim for this paper is to determine whether stress patterns can be recognized using individual-independent computational models from sensor based stress response signals induced by reading text with stressful content. The response signals were obtained by sensors that sourced various physiological and physical signals, from which hundreds of features were derived. The paper proposes feature selection methods to deal with redundant and irrelevant features and improve the performance of classifications obtained from models based on artificial neural networks (ANNs) and support vector machines (SVMs). A genetic algorithm (GA) and a novel method based on pseudo-independence of features are proposed as feature selection methods for the classifiers. Classification performances for the proposed classifiers are compared. The performance of the individual-independent classifiers improved when the feature selection methods were used. The GA-SVM hybrid produced the best results with a stress recognition rate of 98%.
机译：压力是当今世界面临的主要问题，重要的是要客观了解普通人在阅读等典型活动中对压力的反应。本文的目的是确定是否可以使用独立于个人的计算模型，通过读取具有压力内容的文本所引起的基于传感器的压力响应信号来识别压力模式。响应信号是通过传感器获得的，该传感器发出各种生理和物理信号，并从中得出数百个特征。本文提出了特征选择方法来处理冗余和不相关的特征，并提高从基于人工神经网络（ANN）和支持向量机（SVM）的模型获得的分类的性能。提出了一种遗传算法和一种基于特征伪独立性的新方法作为分类器的特征选择方法。比较了建议分类器的分类性能。当使用特征选择方法时，个体独立分类器的性能得到改善。 GA-SVM混合体产生最佳结果，应力识别率为98％。

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