Robust Non-Contact Monitoring of Respiratory Rate using a Depth Camera

被引:5
|
作者
Addison, Paul S. [1 ]
Antunes, Andre [1 ]
Montgomery, Dean [1 ]
Smit, Philip [1 ]
Borg, Ulf R. [2 ]
机构
[1] Technopole Ctr, Medtron Patient Monitoring, Edinburgh, Scotland
[2] Medtron Patient Monitoring, Boulder, CO USA
关键词
Respiratory rate; Non-contact monitoring; Depth-sensing camera;
D O I
10.1007/s10877-023-01003-7
中图分类号
R614 [麻醉学];
学科分类号
100217 ;
摘要
Purpose Respiratory rate (RR) is one of the most common vital signs with numerous clinical uses. It is an important indicator of acute illness and a significant change in RR is often an early indication of a potentially serious complication or clinical event such as respiratory tract infection, respiratory failure and cardiac arrest. Early identification of changes in RR allows for prompt intervention, whereas failing to detect a change may result in poor patient outcomes. Here, we report on the performance of a depth-sensing camera system for the continuous non-contact `touchless' monitoring of Respiratory Rate. Methods Seven healthy subjects undertook a range of breathing rates from 4 to 40 breaths-per-minute (breaths/min). These were set rates of 4, 5, 6, 8, 10, 15, 20, 25, 30, 35 and 40 breaths/min. In total, 553 separate respiratory rate recordings were captured across a range of conditions including body posture, position within the bed, lighting levels and bed coverings. Depth information was acquired from the scene using an Intel D415 RealSense(TM) camera. This data was processed in realtime to extract depth changes within the subject's torso region corresponding to respiratory activity. A respiratory rate RRdepth was calculated using our latest algorithm and output once-per-second from the device and compared to a reference. Results An overall RMSD accuracy of 0.69 breaths/min with a corresponding bias of -0.034 was achieved across the target RR range of 4-40 breaths/min. Bland-Altman analysis revealed limits of agreement of -1.42 to 1.36 breaths/min. Three separate sub-ranges of low, normal and high rates, corresponding to < 12, 12-20, > 20 breaths/min, were also examined separately and each found to demonstrate RMSD accuracies of less than one breath-per-minute. Conclusions We have demonstrated high accuracy in performance for respiratory rate based on a depth camera system. We have shown the ability to perform well at both high and low rates which are clinically important.
引用
收藏
页码:1003 / 1010
页数:8
相关论文
共 50 条
  • [31] Non-contact Heart Rate Monitoring Using Multiple RGB Cameras
    Ghanadian, Hamideh
    Al Osman, Hussein
    [J]. COMPUTER ANALYSIS OF IMAGES AND PATTERNS, CAIP 2019, PT II, 2019, 11679 : 85 - 95
  • [32] Non-Contact Heart Rate Monitoring Using Lab Color Space
    Rahman, Hamidur
    Ahmed, Mobyen Uddin
    Begum, Shahina
    [J]. PHEALTH 2016, 2016, 224 : 46 - 53
  • [33] Video based non-contact monitoring of respiratory rate and chest indrawing in children with pneumonia
    Lucy, Ferdous Karim
    Suha, Khadiza Tun
    Dipty, Sumaiya Tabassum
    Wadud, Md Sharjis Ibne
    Kadir, Muhammad Abdul
    [J]. PHYSIOLOGICAL MEASUREMENT, 2021, 42 (10)
  • [34] Non-Contact Monitoring of Breathing Pattern and Respiratory Rate via RGB Signal Measurement
    Massaroni, Carlo
    Lo Presti, Daniela
    Formica, Domenico
    Silvestri, Sergio
    Schena, Emiliano
    [J]. SENSORS, 2019, 19 (12)
  • [35] NON-CONTACT SLEEP MONITORING WITH A DEPTH CAMERA: MORPHOLOGY SIMILARITY BETWEEN A TOUCHLESS FLOW SIGNAL AND RIPFLOW
    Addison, Paul
    Antunes, Andre
    Montgomery, Dean
    Smit, Philip
    Borg, Ulf
    Brewer, Lara
    Farney, Robert
    Sundar, Krishna
    [J]. SLEEP, 2024, 47
  • [36] Non-Contact Cardio-Pulmonary Resuscitation Compression Action Quality Monitoring Based on Depth Camera
    Geng, Fanglin
    Zhang, Hao
    Yao, Yicheng
    Xia, Pan
    Wang, Peng
    Chen, Xianxiang
    Li, Zhenfeng
    Du, Lidong
    Fang, Zhen
    [J]. 2023 IEEE 19TH INTERNATIONAL CONFERENCE ON BODY SENSOR NETWORKS, BSN, 2023,
  • [37] Non-contact Heart Rate Measurement based on Camera Image
    Kitajima, Toshihiro
    Choi, SangOn
    Murakami, Edwardo Arata Y.
    [J]. JOURNAL OF INFORMATION ASSURANCE AND SECURITY, 2015, 10 (03): : 120 - 129
  • [38] Respiratory signal and human stress: non-contact detection of stress with a low-cost depth sensing camera
    Yuhao Shan
    Shigang Li
    Tong Chen
    [J]. International Journal of Machine Learning and Cybernetics, 2020, 11 : 1825 - 1837
  • [39] Respiratory signal and human stress: non-contact detection of stress with a low-cost depth sensing camera
    Shan, Yuhao
    Li, Shigang
    Chen, Tong
    [J]. INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2020, 11 (08) : 1825 - 1837
  • [40] Non-Contact Infrared-Depth Camera-Based Method for Respiratory Rhythm Measurement While Driving
    Mateu-Mateus, Marc
    Guede-Fernandez, Federico
    Garcia-Gonzalez, Miguel A.
    Ramos-Castro, Juan
    Fernandez-Chimeno, Mireya
    [J]. IEEE ACCESS, 2019, 7 : 152522 - 152532