# Real-Time Stress Detection via Photoplethysmogram Signals: Implementation of a Combined Continuous Wavelet Transform and Convolutional Neural Network on Resource-Constrained Microcontrollers

arXiv: https://arxiv.org/abs/2410.19776 · HTML: https://mlsanity.com/paper/2410.19776/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 20 references that are in the map.
- First posted on arXiv 14 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [GSR Analysis for Stress: Development and Validation of an Open Source Tool for Noisy Naturalistic GSR Data](https://mlsanity.com/paper/2005.01834/) (2020)
- [Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor](https://mlsanity.com/paper/2108.03166/) (2021)
- [Stress Detection Using PPG Signal and Combined Deep CNN-MLP Network](https://mlsanity.com/paper/2410.07911/) (2024)

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

- [Multimodal signal fusion for stress detection using deep neural networks: a novel approach for converting 1D signals to unified 2D images](https://mlsanity.com/paper/2509.13636/) (2025)
- [Artifact Annotations Partially Substitute for Per-User Calibration: SAFE-EDA and a Normalization-Controlled Evaluation of Wrist-EDA Affect Recognition](https://mlsanity.com/paper/2610.01692/) (2026)

## Abstract (arXiv)

This paper introduces a robust stress detection system utilizing a Convolutional Neural Network (CNN) designed for the analysis of Photoplethysmogram (PPG) signals. Employing the WESAD dataset, we applied Continuous Wavelet Transform (CWT) to extract informative features from wrist PPG signals, demonstrating enhanced stress detection and learning compared to conventional techniques. Notably, the CNN achieved an impressive accuracy of 93.7% after five epochs, post-implementation on a resource-constrained microcontroller. The optimization process, including pruning and Post-Train Quantization, was crucial to reduce the model size to 1.6 megabytes, overcoming the microcontroller's limited resources of 2 megabytes of Flash memory and 512 kilobytes of RAM. This optimized model not only addresses resource constraints but also outperforms traditional signal processing methods, positioning it as a promising solution for real-time stress monitoring on wearable devices.