Non-contact physiological parameter detection based on the crested porcupine optimization
This paper introduces a novel non-contact vital sign detection (NC-VSD) method using frequency-modulated continuous-wave (FMCW) millimeter-wave radar. The approach addresses limitations of traditional contact-based methods by leveraging radar technology to detect subtle movements caused by breathing...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825006258 |
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| Summary: | This paper introduces a novel non-contact vital sign detection (NC-VSD) method using frequency-modulated continuous-wave (FMCW) millimeter-wave radar. The approach addresses limitations of traditional contact-based methods by leveraging radar technology to detect subtle movements caused by breathing and heartbeat, offering a user-friendly solution for applications in healthcare, smart homes, and emergency rescue.The methodology involves preprocessing radar signals to extract phase information, followed by optimization of Variational Mode Decomposition (VMD) using the Crested Porcupine Optimization (CPO) algorithm. This hybrid approach effectively removes noise and isolates respiratory and heartbeat signals. Fast Fourier Transform (FFT) is then applied to estimate the corresponding vital signs, yielding respiratory and heartbeat rates with high accuracy. Experiments using the 77 GHz IWR6843EVM radar chip demonstrate the method's effectiveness. After denoising, the average percentage error for respiratory rate extraction is 2.9 %, and for heartbeat rate extraction, it is 7.8 %. These results highlight the method's superiority over traditional approaches such as digital filtering, Empirical Mode Decomposition (EMD), and adaptive energy entropy-based denoising in terms of accuracy and reliability. The study concludes that the proposed NC-VSD method provides a robust and accurate solution for non-contact physiological monitoring. Its non-invasive nature makes it ideal for continuous health monitoring in medical and home settings. However, further research is needed to enhance the algorithm's adaptability to diverse environments and physiological states, ensuring its stability and robustness in real-world applications. |
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| ISSN: | 1110-0168 |