PPG信号用于高温高湿伤害预警的可行性研究

王梦飞; 金龙哲; 于露; 徐明伟

doi:10.3788/OPE.XXXXXXXX.0001

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PPG信号用于高温高湿伤害预警的可行性研究

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- PPG信号用于高温高湿伤害预警的可行性研究
- Feasibility study of PPG signal used in early warning of high temperature and humidity damage
- 液晶与显示 2020年页码：1-8
- 作者机构：
  
  北京科技大学土木与资源工程学院，北京，100083
- 作者简介：
- 基金信息：
  
  国家“十三五”重点科技支撑项目(2016YFC0801700)
- DOI：10.3788/OPE.XXXXXXXX.0001
  中图分类号： X914
- 收稿日期：2019-07-18，
  
  修回日期：2019-10-12，
- 稿件说明：
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王梦飞, 金龙哲, 于露, 等. PPG信号用于高温高湿伤害预警的可行性研究[J/OL]. 液晶与显示, 2020,1-8.

Mengfei WANG, Longzhe JIN, Lu YU, et al. Feasibility study of PPG signal used in early warning of high temperature and humidity damage[J/OL]. Chinese journal of liquid crystals and displays, 2020, 1-8.
王梦飞, 金龙哲, 于露, 等. PPG信号用于高温高湿伤害预警的可行性研究[J/OL]. 液晶与显示, 2020,1-8. DOI： 10.3788/OPE.XXXXXXXX.0001.

Mengfei WANG, Longzhe JIN, Lu YU, et al. Feasibility study of PPG signal used in early warning of high temperature and humidity damage[J/OL]. Chinese journal of liquid crystals and displays, 2020, 1-8. DOI： 10.3788/OPE.XXXXXXXX.0001.

摘要

考虑到高温高湿环境对人体产生不良影响，影响安全生产，对光电容积脉搏波(PPG)信号用于识别人体进入高温高湿环境的可行性进行研究。采集人体处于高温高湿环境和舒适环境的光电容积脉搏波信号，将信号人工标记后输入残差神经网络进行训练，获得预警模型。研究结果表明：人体在高温高湿环境和舒适环境下光电容积脉搏波信号存在差异，这种差异可以被预警模型识别；训练集识别准确率为99.8%，测试集识别准确率为98.3%，训练集和测试集对高温高湿环境的查准率分别为99.8%和96.9%，查全率分别为99.8%和99.7%；训练结果的受试者工作特征曲线(ROC)与横坐标所围成的面积

接近于1，因而认为该模型有较强的泛化能力；所使用的方法预警时间为0.512 s，即在人体生理参数变化后0.512 s对人员发出预警。

Abstract

High temperature and high humidity environment has adverse effects on human body and affects safety production. Photoplethysmography(PPG) signal was used to identify whether human body enters high temperature and high humidity environment. The photoplethysmography signals of human body in high temperature and humidity environment and comfortable environment were collected.The signals were labeled artificially and then input into residual neural network for training

and an early warning model was obtained. The results show that there are differences in photoplethysmography between human body in high temperature and humidity environment and comfortable environment

which can be recognized by early warning model. The recognition accuracy of training set is 99.8%

and that of test set is 98.3%. The precision of training set and test set to high temperature and humidity environment are 99.8% and 96.9%

respectively. The recall rate of training set and test set to high temperature and humidity environment are 99.8% and 99.7%

respectively.The area

enclosed by the ROC curve and the abscissa is close to 1

so the model has strong generalization ability. The early warning time of the method used in this paper is 0.512 s

i.e.

warning is given to human after 0.512 s of the change of human physiological parameters.

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