# Classification of Perceived Human Stress using Physiological Signals

arXiv: https://arxiv.org/abs/1905.06384 · HTML: https://mlsanity.com/paper/1905.06384/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 May 2019.

## Built on by (most cited first, 2 of 2)

- [Human Stress Assessment: A Comprehensive Review of Methods Using Wearable Sensors and Non-wearable Techniques](https://mlsanity.com/paper/2202.03033/) (2022)
- [Machine Learning, Deep Learning and Data Preprocessing Techniques for Detection, Prediction, and Monitoring of Stress and Stress-related Mental Disorders: A Scoping Review](https://mlsanity.com/paper/2308.04616/) (2023)

## Abstract (arXiv)

In this paper, we present an experimental study for the classification of perceived human stress using non-invasive physiological signals. These include electroencephalography (EEG), galvanic skin response (GSR), and photoplethysmography (PPG). We conducted experiments consisting of steps including data acquisition, feature extraction, and perceived human stress classification. The physiological data of 28 participants are acquired in an open eye condition for a duration of three minutes. Four different features are extracted in time domain from EEG, GSR and PPG signals and classification is performed using multiple classifiers including support vector machine, the Naive Bayes, and multi-layer perceptron (MLP). The best classification accuracy of 75% is achieved by using MLP classifier. Our experimental results have shown that our proposed scheme outperforms existing perceived stress classification methods, where no stress inducers are used.