TY - JOUR
T1 - A real-time camera-based adaptive breathing monitoring system
AU - Lee, Yu Ching
AU - Syakura, Abdan
AU - Khalil, Muhammad Adil
AU - Wu, Ching Ho
AU - Ding, Yi Fang
AU - Wang, Ching Wei
N1 - Funding Information:
This study is supported by the Ministry of Science and Technology of Taiwan (MOST 108-2221-E-011-070, MOST109-2221-E-011-018-MY3), Tri-Service General Hospital-National Taiwan University of Science and Technology (TSGH-NTUST-103-02), and the Wan Fang Hospital (107-wf-swf-03).
Publisher Copyright:
© 2021, International Federation for Medical and Biological Engineering.
PY - 2021/6
Y1 - 2021/6
N2 - 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. [Figure not available: see fulltext.]
AB - 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. [Figure not available: see fulltext.]
KW - Non-contact-based breathing monitoring
KW - Respiration rate measurement
KW - Vision-based respiratory rate
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U2 - 10.1007/s11517-021-02371-5
DO - 10.1007/s11517-021-02371-5
M3 - Article
C2 - 34101126
AN - SCOPUS:85107694769
SN - 0140-0118
VL - 59
SP - 1285
EP - 1298
JO - Medical and Biological Engineering and Computing
JF - Medical and Biological Engineering and Computing
IS - 6
ER -