Paper ID: 2410.19776 • Published Oct 14, 2024
Real-Time Stress Detection via Photoplethysmogram Signals: Implementation of a Combined Continuous Wavelet Transform and Convolutional Neural Network on Resource-Constrained Microcontrollers
Yasin Hasanpoor, Amin Rostami, Bahram Tarvirdizadeh, Khalil Alipour, Mohammad Ghamari
TL;DR
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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.