GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data

TL;DR

This paper introduces an open-source tool for analyzing Galvanic Skin Response signals using deep learning and statistical methods, achieving 92% accuracy in stress detection with the WESAD dataset.

Contribution

The paper presents a novel open-source GSR analysis tool combining deep learning and statistical features for improved stress detection.

Findings

Achieved 92% accuracy in stress detection.

Validated the tool using the WESAD dataset.

Demonstrated effectiveness of combined deep learning and statistical features.

Abstract

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.