Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals
This addresses stress monitoring for healthcare applications, but it is incremental as it builds on existing methods like VGG16.
The paper tackled stress classification using PPG signals by combining Continuous Wavelet Transformation with VGG16, achieving a maximum training accuracy of 98% and an average of 96%.
Our research introduces a groundbreaking approach to stress classification through Photoplethysmogram (PPG) signals. By combining Continuous Wavelet Transformation (CWT) with the proven VGG16 classifier, our method enhances stress assessment accuracy and reliability. Previous studies highlighted the importance of physiological signal analysis, yet precise stress classification remains a challenge. Our approach addresses this by incorporating robust data preprocessing with a Kalman filter and a sophisticated neural network architecture. Experimental results showcase exceptional performance, achieving a maximum training accuracy of 98% and maintaining an impressive average training accuracy of 96% across diverse stress scenarios. These results demonstrate the practicality and promise of our method in advancing stress monitoring systems and stress alarm sensors, contributing significantly to stress classification.