  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.

Human-Computer Interaction;Machine Learning;Signal Processing