Real-Time Non-Contact Millimeter Wave Radar-Based Vital Sign Detection

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7560
• Main Authors: Gao, Zhiqiang, Ali, Luqman, Wang, Cong, Liu, Ruizhi, Wang, Chunwei, Qian, Cheng, Sung, Hokun, Meng, Fanyi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 06.10.2022; MDPI
• Subjects: Algorithms; Antennas; detection; Doppler frequency shift; Electrocardiography; Field programmable gate arrays; Fourier transform; heartbeat detection; Methods; mm-wave radar; Physiology; Radar systems; Receivers & amplifiers; Respiration; Sensors; Signal processing; Transmitters
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22197560

In this paper, the extraction of the life activity spectrum based on the millimeter (mm) wave radar is designed to realize the detection of target objects and the threshold trigger module. The maximum likelihood estimation method is selected to complete the design of the average early warning probability trigger function. The threshold trigger module is designed for the echo signal of static objects in the echo signal. It will interfere with the extraction of Doppler frequency shift results. The moving target detection method is selected, and the filter is designed. The static clutter interference is filtered without affecting the phase difference between the detection sequences, and the highlight target signal is improved. The frequency and displacement of thoracic movement are used as the detection data. Through the Fourier transform calculation of the sequence, the spectrum value is extracted within the estimated range of the heartbeat and respiration spectrum, and the heartbeat and respiration signals are picked up. The proposed design uses Modelsim and Quartus for CO-simulation to complete the simulation verification of the function, extract the number of logical units occupied by computing resources, and verify the algorithm with the vital signs experiment. The heartbeat and respiration were detected using the sports bracelet; the relative errors of heartbeat detection were 0–6.3%, the respiration detection was 0–9.5%, and the relative errors of heartbeat detection were overwhelmingly less than 5%.