用指尖脉搏信号识别压力，准确率达82%
Stress Detection Using PPG Signal and Combined Deep CNN-MLP Network
- 结合卷积与全连接网络分析脉搏波信号
- 在UBFC-Phys数据集上达到82%检测准确率
- 适合可穿戴设备压力监测场景
压力已成为现代人生活中的普遍现象，对呼吸、心血管乃至生殖系统等重要生理功能产生显著影响。早期识别压力事件有助于避免其对身体系统的损害。利用生理信号进行压力检测具有重要意义，其中脉搏波（PPG）信号因采集便捷且信息丰富，成为常用信号之一。本研究基于最新公开数据集UBFC-Phys的PPG信号，采用深度卷积神经网络与多层感知机（CNN-MLP）融合模型实现压力检测。实验结果表明，该模型在压力事件识别上达到了约82%的准确率。
原文摘要 · Abstract (English)
Stress has become a fact in people's lives. It has a significant effect on the function of body systems and many key systems of the body including respiratory, cardiovascular, and even reproductive systems are impacted by stress. It can be very helpful to detect stress episodes in early steps of its appearance to avoid damages it can cause to body systems. Using physiological signals can be useful for stress detection as they reflect very important information about the human body. PPG signal due to its advantages is one of the mostly used signal in this field. In this research work, we take advantage of PPG signals to detect stress events. The PPG signals used in this work are collected from one of the newest publicly available datasets named as UBFC-Phys and a model is developed by using CNN-MLP deep learning algorithm. The results obtained from the proposed model indicate that stress can be detected with an accuracy of approximately 82 percent.
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