Key result
An ultra-wideband radar life detection algorithm based on sample entropy and singular spectrum decomposition accurately extracted distance and vital signs information from 5 volunteers.
Why the study?
When detecting vital sign signals with pulsed ultra-wideband radar, weak vital sign echo signals are often obscured by noise, impairing human target identification and localization.
A novel UWB radar algorithm combining sample entropy and singular spectrum decomposition can accurately extract human target distance and vital signs, offering potential for noncontact life detection.
Answers are grounded in this paper and cite their sources.
May aid noncontact vital sign detection in noisy settings; leaves open clinical validation before any practice adoption.
Using pulsed ultra-wideband (UWB) radar noncontact detection technology, when detecting vital sign signals, the weak vital sign echo signals are often covered by various noises, resulting in the inability to identify and locate human targets. To solve this problem, an UWB radar life detection algorithm based on the combination of sample entropy (SE) and singular spectrum decomposition (SSD) is proposed. The human target position information is determined according to the SE value of the radar received pulse. In addition, the respiration and heart rate information were obtained using the SSD method. Fifteen groups of experiments were carried out, and the echo radar signals of five volunteers at three distances were collected, respectively. The measured data analysis results show that the proposed algorithm can accurately and effectively extract the distance and vital signs information of human targets and has extensive research value and application prospects.
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Xin et al. (2023) studied this question. UWB radar life detection algorithm based on SE and SSD was evaluated on Extraction of distance and vital signs information (respiration and heart rate). An ultra-wideband radar life detection algorithm based on sample entropy and singular spectrum decomposition accurately extracted distance and vital signs information from 5 volunteers.
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