کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

صفحه اصلی / نهمین کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals

نویسندگان :

Yasin Hasanpoor¹ Bahram Tarvirdizadeh² Khalil Alipour³ Mohammad Ghamari⁴

1- University of Tehran 2- University of Tehran 3- University of Tehran 4- Kettering University

کلمات کلیدی :

Continues Wavelet Transform (CWT)،Photoplethysmogram (PPG)،Kalman Filter،Stress Assessment،Visual Geometry Group 16 (VGG16)

چکیده :

Abstract— 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.