A real-time camera-based adaptive breathing monitoring system

• Published in: Medical & biological engineering & computing Vol. 59; no. 6; pp. 1285 - 1298
• Main Authors: Lee, Yu-Ching, Syakura, Abdan, Khalil, Muhammad Adil, Wu, Ching-Ho, Ding, Yi-Fang, Wang, Ching-Wei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Algorithms; Background noise; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Breathing; Cameras; Computer Applications; Correlation coefficients; Human Physiology; Humans; Imaging; Medical equipment; Monitoring; Monitoring systems; Monitoring, Physiologic; Motion; Motion detection; Motion perception; Movement; Original; Original Article; Radiology; Real time; Respiration; Respiratory Rate; Telemedicine; Non-contact-based breathing monitoring; Respiration rate measurement; Vision-based respiratory rate
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-021-02371-5

Breathing is one of the vital signs used to assess the physical health of a subject. Non-contact-based measurements of both breathing rate and changes in breathing rate help monitor health condition of subjects more flexibly. In this paper, we present an improved real-time camera-based adaptive breathing monitoring system, which includes real time (1) adaptive breathing motion detection, (2) adaptive region of interest detection to eliminate environmental noise, (3) breathing and body movement classification, (4) respiration rate estimation, (5) monitor change in respiration rate to examine overall health of an individual, and (6) online adaptation to lighting. The proposed system does not pose any positional and postural constraint. For evaluation, 30 videos of 15 animals are tested with drugs to simulate various medical conditions and breathing patterns, and the results from the proposed system are compared with the outputs of an existing FDA-approved invasive medical system for patient monitoring. The results show that the proposed method performs significantly correlated RR results to the reference medical device with the correlation coefficient equal to 0.92 and p -value less than 0.001, and more importantly the proposed video-based method is demonstrated to produce alarms 10 to 20 s earlier than the benchmark medical device. Graphical abstract The proposed system flowchart to extract the respiratory pattern from video.