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

• URL: http://arxiv.org/abs/2005.01834v3
• Date: Wed, 1 Jul 2020 19:06:12 GMT
• Title: GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data
• Authors: Seyed Amir Hossein Aqajari (1), Emad Kasaeyan Naeini (1), Milad Asgari Mehrabadi (1), Sina Labbaf (1), Amir M. Rahmani (1 and 2), Nikil Dutt (1) ((1) Department of Computer Science, University of California, Irvine, (2) School of Nursing, University of California, Irvine)
• Abstract summary: Galvanic Skin Response (GSR), also known as Electrodermal Activity (EDA), is one of the leading indicators for stress. 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. The results show that we are capable of detecting stress with the accuracy of 92 percent using 10-fold cross-validation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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