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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读取压力的计算模型

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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：压力是我们今天的世界面临的主要问题，重要的是制定客观理解平均个人在阅读等典型活动中如何应对压力。本文的目的是确定是否可以使用通过通过读取文本引起的传感器的应力响应信号来识别应力模式是否可以识别来自传感器的应力响应信号。通过传感器获得响应信号，该传感器可以从中源各种生理和物理信号，从而导出数百个特征。本文提出了应对冗余和无关的特征的特征选择方法，提高基于人工神经网络（ANNS）和支持向量机（SVM）的模型中获得的分类的性能。提出了一种基于特征伪独立性的遗传算法（GA）和一种新方法作为分类器的特征选择方法。比较了所提出的分类器的分类性能。当使用特征选择方法时，个人独立分类器的性能改善。 GA-SVM杂交机产生了最佳效果，应力识别率为98％。

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