论文标题
GSR压力分析：开发和验证嘈杂自然主义GSR数据的开源工具
GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data
论文作者
论文摘要
压力检测问题是在相关研究社区中受到极大关注。这是由于它在许多严重的健康问题和身体疾病的行为研究中至关重要的原因。使用不同的生理信号有不同的方法和算法来检测压力。先前的研究已经表明，电力皮肤反应（GSR）（GSR），也称为电肌活动（EDA），是应力的主要指标之一。但是，GSR信号本身并不容易分析。从GSR信号中提取不同的特征，以检测诸如峰值，最大峰值振幅等人的压力。在本文中，我们提出了一种用于GSR分析的开源工具，该工具使用深度学习算法以及统计算法以及提取GSR特征的统计算法来提取应力检测。然后，我们使用不同的机器学习算法和可穿戴压力，并影响检测（WESAD）数据集来评估我们的结果。结果表明，我们能够使用10倍的交叉验证以及使用从工具中提取的功能来以92％的精度检测应力。
The stress detection problem is receiving great attention in related research communities. This is due to its essential part in behavioral studies for many serious health problems and physical illnesses. There are different methods and algorithms for stress detection using different physiological signals. Previous studies have already shown that Galvanic Skin Response (GSR), also known as Electrodermal Activity (EDA), is one of the leading indicators for stress. However, the GSR signal itself is not trivial to analyze. Different features are extracted from GSR signals to detect stress in people like the number of peaks, max peak amplitude, etc. In this paper, we are proposing an open-source tool for GSR analysis, which uses deep learning algorithms alongside statistical algorithms to extract GSR features for stress detection. Then we use different machine learning algorithms and Wearable Stress and Affect Detection (WESAD) dataset to evaluate our results. The results show that we are capable of detecting stress with the accuracy of 92 percent using 10-fold cross-validation and using the features extracted from our tool.
