Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras
Millimeter wave radars in frequency-modulated continuous wave (FMCW) systems are widely used in the field of noncontact life signal detection; however, large errors still persist when determining the distance dimension of the target to be measured with the radar echo signal. The processing of the si...
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| Format: | Article |
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MDPI AG
2023-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/10/6270 |
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| author | Jiale Dai Jiahui Yan Yaolong Qi |
| author_facet | Jiale Dai Jiahui Yan Yaolong Qi |
| author_sort | Jiale Dai |
| collection | DOAJ |
| description | Millimeter wave radars in frequency-modulated continuous wave (FMCW) systems are widely used in the field of noncontact life signal detection; however, large errors still persist when determining the distance dimension of the target to be measured with the radar echo signal. The processing of the signals in the target environment is blind. We propose a method of using binocular vision to lock the distance dimension of the radar life signal and to determine the target distance by using the principle of the binocular camera parallax method, as this reduces the influence of the noise in the environment when determining the distance dimension of the target to be measured. First, the Yolo (you only look once: unified, real-time object detection) v5s neural network is used to call the binocular camera to detect the human body, where the resolution of the single lens is 1280 × 1200, and the DeepSORT (deep simple online real-time tracking) algorithm is used to extract the features of the target and track and register them. Additionally, the binocular vision parallax ranging method is used to detect the depth information of the target, search for the depth information in the range-dimensional FFT (frequency Fourier transform) spectrum of the radar echo signal, and take the spectral peak with the largest energy within the search range to determine it as the target. Then, the target is measured, the range gate of the target is determined, and the life signal is then separated through operations such as phase information extraction, unwrapping, and filtering. The test results showed that this method can be used to directionally separate and register corresponding life signals in a multiliving environment. By conducting an analysis using the Pearson correlation coefficient, we found that the correlation between the breathing frequency collected using this method and a breathing sensor reached 84.9%, and the correlation between the heartbeat frequency and smart bracelet results reached 93.6%. The target range gate was locked to separate and match the life signal. |
| first_indexed | 2024-03-11T03:57:24Z |
| format | Article |
| id | doaj.art-1bad794d165a4a44866e38f37a5765a7 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T03:57:24Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1bad794d165a4a44866e38f37a5765a72023-11-18T00:23:06ZengMDPI AGApplied Sciences2076-34172023-05-011310627010.3390/app13106270Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular CamerasJiale Dai0Jiahui Yan1Yaolong Qi2College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaCollege of Information Engineering, Inner Mongolia University of Technology, Hohhot 010051, ChinaMillimeter wave radars in frequency-modulated continuous wave (FMCW) systems are widely used in the field of noncontact life signal detection; however, large errors still persist when determining the distance dimension of the target to be measured with the radar echo signal. The processing of the signals in the target environment is blind. We propose a method of using binocular vision to lock the distance dimension of the radar life signal and to determine the target distance by using the principle of the binocular camera parallax method, as this reduces the influence of the noise in the environment when determining the distance dimension of the target to be measured. First, the Yolo (you only look once: unified, real-time object detection) v5s neural network is used to call the binocular camera to detect the human body, where the resolution of the single lens is 1280 × 1200, and the DeepSORT (deep simple online real-time tracking) algorithm is used to extract the features of the target and track and register them. Additionally, the binocular vision parallax ranging method is used to detect the depth information of the target, search for the depth information in the range-dimensional FFT (frequency Fourier transform) spectrum of the radar echo signal, and take the spectral peak with the largest energy within the search range to determine it as the target. Then, the target is measured, the range gate of the target is determined, and the life signal is then separated through operations such as phase information extraction, unwrapping, and filtering. The test results showed that this method can be used to directionally separate and register corresponding life signals in a multiliving environment. By conducting an analysis using the Pearson correlation coefficient, we found that the correlation between the breathing frequency collected using this method and a breathing sensor reached 84.9%, and the correlation between the heartbeat frequency and smart bracelet results reached 93.6%. The target range gate was locked to separate and match the life signal.https://www.mdpi.com/2076-3417/13/10/6270non-contact vital signal detectionmm-wave radarYolo v5sDeepSORT algorithmbinocular visiondistance measurement by parallax |
| spellingShingle | Jiale Dai Jiahui Yan Yaolong Qi Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras Applied Sciences non-contact vital signal detection mm-wave radar Yolo v5s DeepSORT algorithm binocular vision distance measurement by parallax |
| title | Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras |
| title_full | Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras |
| title_fullStr | Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras |
| title_full_unstemmed | Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras |
| title_short | Millimeter Wave Radar Range Bin Tracking and Locking for Vital Sign Detection with Binocular Cameras |
| title_sort | millimeter wave radar range bin tracking and locking for vital sign detection with binocular cameras |
| topic | non-contact vital signal detection mm-wave radar Yolo v5s DeepSORT algorithm binocular vision distance measurement by parallax |
| url | https://www.mdpi.com/2076-3417/13/10/6270 |
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