用小波变换与VGG16分析心率信号，准确识别压力状态。
Continuous Wavelet Transformation and VGG16 Deep Neural Network for Stress Classification in PPG Signals
- 先用小波变换提取生理信号特征，再用VGG16分类
- 训练最高准确率达98%，平均达96%
- 适合开发实时压力监测设备
本研究提出一种基于光电容积脉搏波（PPG）信号进行压力分类的创新方法。通过结合连续小波变换（CWT）与成熟的VGG16分类器，显著提升了压力评估的准确性与可靠性。针对以往研究中生理信号分析的重要性，但精确分类仍具挑战的问题，本方法引入卡尔曼滤波进行稳健的数据预处理，并采用先进的神经网络架构。实验结果表明，该方法在多种压力场景下表现优异，最大训练准确率达到98%，平均训练准确率为96%。这些结果验证了该方法在压力监测系统与压力报警传感器中的实用价值与前景，对推动压力分类技术发展具有重要意义。
原文摘要 · Abstract (English)
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.
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