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

arXiv: https://arxiv.org/abs/2005.01834 · HTML: https://mlsanity.com/paper/2005.01834/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2020.

## Built on by (most cited first, 8 of 8)

- [AttX: Attentive Cross-Connections for Fusion of Wearable Signals in Emotion Recognition](https://mlsanity.com/paper/2206.04625/) (2022)
- [Stress Classification and Personalization: Getting the most out of the least](https://mlsanity.com/paper/2107.05666/) (2021)
- [Edge-centric Optimization of Multi-modal ML-driven eHealth Applications](https://mlsanity.com/paper/2208.02597/) (2022)
- [CUDLE: Learning Under Label Scarcity to Detect Cannabis Use in Uncontrolled Environments](https://mlsanity.com/paper/2410.03211/) (2024)
- [Real-Time Stress Detection via Photoplethysmogram Signals: Implementation of a Combined Continuous Wavelet Transform and Convolutional Neural Network on Resource-Constrained Microcontrollers](https://mlsanity.com/paper/2410.19776/) (2024)
- [Exploration of Low-Power Flexible Stress Monitoring Classifiers for Conformal Wearables](https://mlsanity.com/paper/2508.19661/) (2025)
- [MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals](https://mlsanity.com/paper/2410.04992/) (2024)
- [Bespoke Co-processor for Energy-Efficient Health Monitoring on RISC-V-based Flexible Wearables](https://mlsanity.com/paper/2511.05985/) (2025)

## Abstract (arXiv)

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.