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
Addressing challenges such as low performance,high data signal-to-noise ratio requirements,and limited real-time capabilities in existing heart rate detection methods based on millimeter wave radar,this study presents...Addressing challenges such as low performance,high data signal-to-noise ratio requirements,and limited real-time capabilities in existing heart rate detection methods based on millimeter wave radar,this study presents a heart rate sensing approach tailored for weak vital sign signals characterized by low signal-to-noise ratio and missing data.The method applies a signal mask for echo sequences with variable length.Building upon this signal mask,a signal mapping technique that leverages morphology is devised to mitigate interference and noise.Additionally,learnable position encoding is incorporated to capture temporal features within the signal.Subsequently,a transformer encoder module is employed for matching and computation,culminating in the development of a time-series global regression model based on deep learning framework.Following the preparation of the dataset and model training,the proposed approach is validated by performance analysis experiments,interference resistance tests,and comparative experiments.Results indicate that this method achieves an impressive accuracy of 96.30%within signal durations ranging from 2 s to 5 s,and it is suitable for scenarios involving missing data and noise interference.Importantly,this approach effectively enables a precise heart rate sensing from short-duration radar signals.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%.
基金the National Natural Science Foundation of China(No.51975361)。
文摘Addressing challenges such as low performance,high data signal-to-noise ratio requirements,and limited real-time capabilities in existing heart rate detection methods based on millimeter wave radar,this study presents a heart rate sensing approach tailored for weak vital sign signals characterized by low signal-to-noise ratio and missing data.The method applies a signal mask for echo sequences with variable length.Building upon this signal mask,a signal mapping technique that leverages morphology is devised to mitigate interference and noise.Additionally,learnable position encoding is incorporated to capture temporal features within the signal.Subsequently,a transformer encoder module is employed for matching and computation,culminating in the development of a time-series global regression model based on deep learning framework.Following the preparation of the dataset and model training,the proposed approach is validated by performance analysis experiments,interference resistance tests,and comparative experiments.Results indicate that this method achieves an impressive accuracy of 96.30%within signal durations ranging from 2 s to 5 s,and it is suitable for scenarios involving missing data and noise interference.Importantly,this approach effectively enables a precise heart rate sensing from short-duration radar signals.
