Vital Sign Detection during Large-Scale and Fast Body Movements Based on an Adaptive Noise Cancellation Algorithm Using a Single Doppler Radar Sensor.

Zi-Kai Yang,Heping Shi,Xiang-Dong Huang,Sheng Zhao

doi:10.3390/s20154183

Abstract

The non-contact detection of human vital signs (i.e., respiration rate (RR) and heartbeat rate (HR)) using a continuous-wave (CW) Doppler radar sensor has great potential for intensive care monitoring, home healthcare, etc. However, large-scale and fast random body movement (RBM) has been a bottleneck for vital sign detection using a single CW Doppler radar. To break this dilemma, this study proposed a scheme combining adaptive noise cancellation (ANC) with polynomial fitting, which could retrieve the weak components of both respiration and heartbeat signals that were submerged under serious RBM interference. In addition, the new-type discrete cosine transform (N-DCT) was introduced to improve the detection accuracy. This scheme was first verified using a numerical simulation. Then, experiments utilizing a 10-GHz Doppler radar sensor that was built from general-purpose radio frequency (RF) and communication instruments were also carried out. No extra RF/microwave components and modules were needed, and neither was a printed circuit board nor an integrated-chip design required. The experimental results showed that both the RR and HR could still be extracted during large-scale and fast body movements using only a single Doppler radar sensor because the RBM noises could be greatly eliminated by utilizing the proposed ANC algorithm.

Highlights

Phase-modulation microwave Doppler radar sensors [1], which are attractive due to the advantages of being contactless and able to penetrate obstacles [2], have gained increasing attention in the field of monitoring vital signs, i.e., respiration rate (RR) and heartbeat rate (HR) [3,4,5,6,7,8]
The Doppler radar sensor transmits a single-tone CW signal to a subject and receives the reflected signals, which carry the information of the physiological movements of the subject in its phase
frequency of the Transform (FFT) method the new-type discrete cosine transform (N-DCT) reduced the average error of the RR from 24.77% to 4.86% and reduced the average error of

Summary

Introduction

Phase-modulation microwave Doppler radar sensors [1], which are attractive due to the advantages of being contactless and able to penetrate obstacles [2], have gained increasing attention in the field of monitoring vital signs, i.e., respiration rate (RR) and heartbeat rate (HR) [3,4,5,6,7,8]. The Doppler radar sensor transmits a single-tone CW signal to a subject and receives the reflected signals, which carry the information of the physiological movements of the subject in its phase. In the receiver of the sensor, the information regarding the RR and HR of the subject can be extracted by demodulating the received signals. This technique enables short-distance vital sign detection of humans without contacting the testers. With the non-contact vital sign detection capability, the CW Doppler radar sensor has great potential for intensive care monitoring [9], home healthcare, and long-term monitoring applications [10]. Great theoretical and technological efforts have been made in the past few decades [11,12,13,14,15], such as the use of machine learning algorithms to improve the detection accuracy of HR [15], Sensors 2020, 20, 4183; doi:10.3390/s20154183 www.mdpi.com/journal/sensors

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Results

Discussion

Conclusion