[1]徐展宇,陈兆学.基于改进三维卷积网络的非接触式生理参数检测方法[J].中国医学物理学杂志,2025,42(4):479-488.[doi:10.3969/j.issn.1005-202X.2025.04.009]
　XU Zhanyu,CHEN Zhaoxue.Non-contact physiological parameter detection method based on improved three-dimensionalconvolution network[J].Chinese Journal of Medical Physics,2025,42(4):479-488.[doi:10.3969/j.issn.1005-202X.2025.04.009]
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基于改进三维卷积网络的非接触式生理参数检测方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第4期
页码:
479-488
栏目:
医学信号处理与医学仪器
出版日期:
2025-04-20

文章信息/Info

Title:
Non-contact physiological parameter detection method based on improved three-dimensionalconvolution network
文章编号:
1005-202X（2025）04-0479-10
作者:
徐展宇; 陈兆学
上海理工大学健康科学与工程学院，上海 200093
Author(s):
XU Zhanyu;  CHEN Zhaoxue
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
关键词:
非接触式; 心率检测; 混合注意力机制; 信号处理
Keywords:
non-contact type;  heart rate detection;  hybrid attention mechanism;  signal processing
分类号:
R318；TP391
DOI:
10.3969/j.issn.1005-202X.2025.04.009
文献标志码:
A
摘要:
远程光电容积描记法（rPPG）是从面部视频中测量心率等生理参数的方法，针对现有的心率测量方法难以同时兼顾高准确率和轻量化的问题，提出一种改进的三维卷积网络模型实现基于面部视频的非接触式生理参数检测。在预处理时，使用YuNet模型替代传统人脸检测器，从而快速且精确地识别人脸区域。此外，将注意力机制和残差模块嵌入到三维卷积网络中提取通道和空间的关键特征，并使用长短期记忆网络作为时期记忆模块捕捉数据中的长期依赖关系。实验结果表明，所提出 Res-CHATM 模型在公开数据集 UBFC-rPPG 和 PURE进行心率评估交叉实验时分别取得MAE=2.19 BPM，RMSE=7.02 BPM，C=0.95以及MAE=1.65 BPM，RMSE=3.44 BPM，C=0.98的优异效果，进一步验证了模型预测值与真实值之间的一致性以及融合模块的有效性，展示了高效轻量化模型在rPPG技术中的潜力。
Abstract:
Remote photoplethysmography is a method of measuring physiological parameters such as heart rate from facialvideo. For overcoming the difficulties in achieving both high accuracy and lightweight by the existing heart rate measurementmethods, an improved three-dimensional convolution network model is proposed to realize non-contact physiologicalparameter detection in facial video. In the pre-processing, YuNet model takes place of the traditional face detector, so that theface region can be recognized quickly and accurately. In addition, attention mechanisms and residual modules are embed intothree-dimensional convolution network to extract key channel and spatial features, with long short-term memory networksused as period memory modules to capture long-term dependencies in the data. The experimental results show that theproposed Res-CHATM model achieves excellent results of MAE=2.19 BPM, RMSE=7.02 BPM, C=0.95, and MAE=1.65BPM, RMSE=3.44 BPM, C=0.98 in the cross experiments on public datasets UBFC-rPPG and PURE for heart rateestimation. The consistency between the predicted value and the real value and the effectiveness of the fusion module arefurther verified, demonstrating the potential of efficient lightweight model in remote photoplethysmography.

相似文献/References:

[1]杜振伟,孙 建,秦明新,等.基于磁感应技术的新型非接触心肺信号检测系统的研究[J].中国医学物理学杂志,2014,31(06):5288.[doi:10.3969/j.issn.1005-202X.2014.06.016]
[2]郑小涵,朱岩,杨越琪,等.基于心冲击信号的心率检测方法[J].中国医学物理学杂志,2021,38(11):1405.[doi:DOI:10.3969/j.issn.1005-202X.2021.11.016]
　ZHENG Xiaohan,ZHU Yan,YANG Yueqi,et al.Heart rate detection based on ballistocardiogram signals[J].Chinese Journal of Medical Physics,2021,38(4):1405.[doi:DOI:10.3969/j.issn.1005-202X.2021.11.016]

备注/Memo

备注/Memo:
【收稿日期】2024-11-05【基金项目】国家中医药管理局中医药创新团队及人才支持计划（ZYYCXTD-D-202208）【作者简介】徐展宇，硕士研究生，研究方向：图像处理、深度学习，E-mail: xuzhanyu@foxmail.com【通信作者】陈兆学，博士，副教授，研究方向：医学图像处理，医学信号处理，E-mail: chenzhaoxue@163.com
更新日期/Last Update: 2025-04-30