A Novel Non-Contact Respiration and Heartbeat Detection Method Using Frequency-Modulated Continuous Wave Radar

Wang, Yong; Liu, Heng; Xiang, Wei; Shui, Yuzhu; Guo, Lei; Zhou, Mu; Pang, Yu

doi:10.1109/jsen.2024.3351274

IEEE Sensors J.

2024

DOI: 10.1109/jsen.2024.3351274

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A Novel Non-Contact Respiration and Heartbeat Detection Method Using Frequency-Modulated Continuous Wave Radar

Yong Wang,

Heng Liu,

Wei Xiang

et al.

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2024

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Cited by 3 publications

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Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios

Guan,

Yang,

Zhao

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios

Guan,

Yang,

Zhao

et al. 2024

IEEE J. Transl. Eng. Health Med.

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Energy-Efficient Sparse FFT and Compressed Transpose Memory for mmWave FMCW Radar Sensor System

Harishore Singh,

Huang,

Kao

et al. 2024

IEEE Trans. Instrum. Meas.

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mmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar

Hao,

Wang,

et al. 2024

Sensors

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Breathing is one of the body’s most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring.

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A Novel Non-Contact Respiration and Heartbeat Detection Method Using Frequency-Modulated Continuous Wave Radar

Cited by 3 publications

References 38 publications

Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios

Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios

Energy-Efficient Sparse FFT and Compressed Transpose Memory for mmWave FMCW Radar Sensor System

mmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar

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