面向非稳态场景的生命体征监测优化方法  

An Optimization Method of Human Vital Signs Detection During the Non-Steady States

作　　者：邱杰凡 徐一帆 徐瑞吉 周栋利 池凯凯[1] Qiu Jiefan;Xu Yifan;Xu Ruiji;Zhou Dongli;Chi Kaikai(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023)

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023

出　　处：《计算机研究与发展》2024年第2期481-493,共13页Journal of Computer Research and Development

基　　金：国家自然科学基金面上项目(62272414,61872322);浙江省自然科学基金项目(LY20F020026);浙江省教育厅科研项目(Y202147457)。

摘　　要：使用基于调频连续波的毫米波雷达监测生命体征信息,具有无接触、隐私保护性好、高分辨率以及抗干扰性好等优势,逐渐成为研究热点.然而,目前研究者主要关注如何提高被测对象处于稳态(如静止)时的体征监测效果,但受制于肢体运动对雷达信号的干扰,使得该技术在非稳态场景中的应用受到限制.提出一种基于人体运动状态识别的非稳态场景体征监测方法,以best-effort方式实现了存在大幅度肢体动作的场景中对体征信息的监测,并且能够识别对应的动作类型.首先,根据运动特征计算出带有距离-主导速度信息的特征频谱图.其次,使用一种滑动窗口采样方法以采集连续样本.随后训练ResNet-18网络来识别运动状态以及分类运动类型.最后,基于运动状态分类结果,在运动间歇期提取信号的相位信息,采用变分模态分解算法进行呼吸速率和心率的提取.实验结果表明,训练后的网络可以精确地识别运动状态和运动类型,识别准确率接近97%,识别延迟小于1.1 s.对呼吸和心率的监测结果的平均绝对误差(MAD)下降到1.7 bpm和3.4 bpm.Because the millimeter-wave radar based on frequency-modulated continuous wave(FMCW)owns the advantages of non-contact,privacy protection,high resolution,and anti-interference,applying it in monitoring vital signs gradually becomes a trend.However,most researchers focus on how to improve the detection performance under the steady-state state and give little consideration to the detection under the non-steady state with physical disturbance.We propose a method to detect vital signs in a best-effort way.This method recognizes the steady state and non-steady state,and the type of motion obtains the vital signs without physical disturbance.For this end,we first compute feature spectrograms with range-main velocity information from motion features.And then,employ slide windows sampling to construct datasets,and train the ResNet-18 network to identify the motion state and the motion type.Finally,based on the result of the motion state,we extract the phase signal during exercise rest and use the variational mode decomposition(VMD)algorithm to analyze the respiration rate and heart rate.Experiment results show that using the trained network model,the recognition accuracy of the motion state and motion type is close to 97%,and the recognition delay is less than 1.1 s.Meanwhile,after motion recognition,the mean absolute deviation(MAD)of the detection respiration and heart rate drops to 1.7 bpm and 3.4 bpm respectively.

关 键 词：毫米波雷达 生命体征监测 运动状态识别 滑动窗口采样 残差网络 变分模态分解 

分 类 号：TP212.6[自动化与计算机技术—检测技术与自动化装置]