Research on non-contact respiratory rate measurement method based on video information_Journal of Biomedical Engineering

Authors：

LI Shiqi ,  WANG Haipeng , LIU Yibin

School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, P.R.China;

Corresponding author：

WANG Haipeng, Email: wanghai463100@126.com

Keywords：

non-contact; respiratory rate measurement; gray compensation; low-pass filtering

DOI：

10.7507/1001-5515.202011029

Video：

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Abstract Full text Figures/Tables Video References Cited by

Traditional methods of non-contact human respiratory rate measurement usually require complex devices or algorithms. Aiming at this problem, a non-contact respiratory rate measurement method based on only the RGB video information was proposed in this paper. The method consisted of four steps. Firstly, spatial filtering was applied to each frame of the input video. Secondly, a gray compensation algorithm was used to compensate for the gray level change caused by the environmental light. Thirdly, the gray levels of each pixel over time were filtered separately by a low-pass filter. Finally, the region of interest was determined based on the filtering results, and the respiration rate of the human is measured. The physical measurement experiments were designed, and the measurement accuracy was compared with that of the biological radar. The error of the proposed method was between − 5.5% and 3% in different detection directions. The results show that the non-contact respiration rate measurement method can effectively measure the human respiration rate.

Citation： LI Shiqi, WANG Haipeng, LIU Yibin. Research on non-contact respiratory rate measurement method based on video information. Journal of Biomedical Engineering, 2021, 38(6): 1173-1180. doi: 10.7507/1001-5515.202011029 Copy

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1. Doulamis N, Agrafiotis P, Athanasiou G, et al. Human object detection using very low resolution thermal cameras for urban search and rescue// Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments. Rhodes: University of Texas, 2017: 311-318.
2. Budzan S, Wyżgolik R, Ilewicz W. Improved human detection with a fusion of laser scanner and vision/infrared information for mobile applications. Appl SCI-Basel, 2018, 8(10): 1967.
3. 张鹏飞, 张华, 拜军, 等. 呼吸信号检测技术研究现状. 国际生物医学工程杂志, 2012(6): 365-368, 380.
4. 严旭, 刘洪英, 贾子如, 等. 呼吸频率检测技术研究现状. 北京生物医学工程, 2017, 36(5): 545-549.
5. Zhao M, Li T, Abu Alsheikh M, et al. Through-wall human pose estimation using radio signals// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 7356-7365.
6. Kim Y, Moon T. Human detection and activity classification based on micro-Doppler signatures using deep convolutional neural networks. IEEE Geosci Remote S, 2015, 13(1): 8-12.
7. 梁松, 蔡雨, 王慧泉, 等. 基于微波非接触式呼吸检测系统设计. 生物医学工程研究, 2019, 38(4): 476-480.
8. Wu C, Yang Z, Zhou Z, et al. Non-invasive detection of moving and stationary human with WiFi. IEEE J Sel Area Comm, 2015, 33(11): 2329-2342.
9. Niu K, Zhang F, Chang Z, et al. A fresnel diffraction model based human respiration detection system using COTS Wi-Fi devices// Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers. New York: ACM, 2018: 416-419.
10. Aoki H, Miyazaki M, Nakamura H, et al. Non-contact respiration measurement using structured light 3-d sensor// 2012 Proceedings of SICE annual conference (SICE). Akita: IEEE, 2012: 614-618.
11. Burba N, Bolas M, Krum D M, et al. Unobtrusive measurement of subtle nonverbal behaviors with the Microsoft Kinect// 2012 IEEE Virtual Reality Workshops (VRW). Costa Mesa: IEEE, 2012: 1-4.
12. Lee T S, Tsui B M W. The development and initial evaluation of a realistic simulated SPECT dataset with simultaneous respiratory and cardiac motion for gated myocardial perfusion SPECT. Phys Med Biol, 2015, 60(4): 1399.
13. 单禹皓, 陈通, 温万惠, 等. 呼吸信号的非接触式测量. 计算机科学, 2015, 42(10): 43-44, 75.
14. 赵素文, 高凡, 邓莉. 小波变换结合快速傅里叶变换从PPG中提取呼吸率. 中国医学物理学杂志, 2016, 33(1): 39-43.
15. Villarroel M, Guazzi A, Jorge J, et al. Continuous non-contact vital sign monitoring in neonatal intensive care unit. Healthc Technol Lett, 2014, 1(3): 87-91.
16. Fang C Y, Hsieh H H, Chen S W. A vision-based infant respiratory frequency detection system// 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA). Adelaide: IEEE, 2015: 1-8.
17. Bai Z, Gao Q, Yu X. Moving object detection based on adaptive loci frame difference method// 2019 IEEE International Conference on Mechatronics and Automation (ICMA). Tianjin: IEEE, 2019: 2218-2223.
18. Zhou F, De la Torre F. Spatio-temporal matching for human detection in video// European Conference on Computer Vision. Cham: Springer, 2014: 62-77.
19. Sommer L W, Teutsch M, Schuchert T, et al. A survey on moving object detection for wide area motion imagery// 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Placid: IEEE, 2016: 1-9.
20. Tran Q V, Su S F, Chuang C C, et al. Real-time non-contact breath detection from video using adaboost and Lucas-Kanade algorithm// 2017 Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems (IFSA-SCIS). Otsu: IEEE, 2017: 1-4.
21. Kumdee O, Ritthipravat P. Repetitive motion detection for human behavior understanding from video images// 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT). United Arab Emirates: IEEE, 2015: 484-489.
22. Fung A, Wang L Y, Zhang K, et al. Using deep learning to find victims in unknown cluttered urban search and rescue environments. Curr Robot Rep, 2020, 1(3): 105-115.
23. 闫永强. 用于睡眠监测的非接触式呼吸测量方法及其实现. 深圳: 深圳大学, 2018.
24. Chekmenev S Y, Farag A A, Essock E A. Thermal imaging of the superficial temporal artery: An arterial pulse recovery model// 2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE, 2007: 1-6.
25. Tan K S, Saatchi R, Elphick H, et al. Real-time vision based respiration monitoring system// 2010 7th International Symposium on Communication Systems, Networks & Digital Signal Processing (CSNDSP 2010). Newcastle: IEEE, 2010: 770-774.

Journal of Biomedical Engineering

Research on non-contact respiratory rate measurement method based on video information

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