Intelligent Heart Rate Extraction Method Based on Millimeter Wave Radar

doi:10.1007/s12204-023-2656-1

Abstract

Abstract: The non-contact vital signs measurement technology based on millimeter wave radar has important medical value and unique advantages. However, because of its weak vibration characteristics, wide range of values, and the presence of respiratory harmonics and irrelevant motion interference in the detection signal, it is still difficult to perform a robust extraction in real time. To solve the above problems, the adaptive extraction of heart rates with a wide range of distribution is summarized as a multi-scale detection problem, and the distinction between heartbeat features and other irrelevant body motion features is summarized as a feature attention problem. Then, multi-scale detection module and heart rate feature attention module are designed and combined into a basic network module to build a heart rate extraction neural network. Through experiments based on properly designed datasets, a reasonable parameter design of the module is first explored. Experimental results show that in the signal data with unrelated motion data interference, average absolute error of the proposed method model for heart rate extraction can reach 1.87 beats/min, and average relative accuracy can reach 97.51%.

Key words: millimeter wave radar, vital sign monitoring, heart rate, neural network

摘要： 毫米波雷达的非接触式生命体征测量技术具有重要医用价值与独特优势。然而由于心跳震动特征微弱，频率范围较广，且检测信号存在呼吸谐波及无关运动干扰等因素，进行实时鲁棒提取仍有难点。针对上述问题，将对于广范围分布的快慢心率自适应提取归纳为多尺度检测问题，将区分心跳特征与其他无关身体运动特征归纳为特征关注问题，进行了多尺度检测模块和心率特征关注模块设计，并组合为基本网络模块，搭建成心率提取神经网络。通过合理的数据集设计与模块参数设计进行实验，其结果表明，在有无关运动数据干扰的信号数据中，所提方法模型进行心率提取时的绝对误差平均可以达到1.87次/分，相对准确率平均可以达到97.51%。

关键词: 毫米波雷达，生命体征监测，心率，神经网络

CLC Number:

TP391
R318.6

Feng Lingdong, Miao Yubin. Intelligent Heart Rate Extraction Method Based on Millimeter Wave Radar[J]. J Shanghai Jiaotong Univ Sci, 2025, 30(3): 493-498.

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