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 pr...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%.展开更多
基金the National Natural Science Foundation of China(No.51975361)。
文摘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%.
