Patient Posture-Adaptive Non-Contact Respiratory Rate Measurement Inside a Moving Ambulance Using RGB and Thermal Cameras.
4 affiliations
- 1Department of Medical Engineering, Graduate School of Science and Engineering, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba-shi 263-8522, Chiba, Japan.
- 2Department of Emergency and Critical Care Medicine, Graduate School of Medicine, Chiba University, 1-8-1, Inohana, Chuo-ku, Chiba-shi 260-8670, Chiba, Japan.
- 3Department of Obstetrics and Gynecology, Chiba University Hospital, 1-8-1, Inohana, Chuo-ku, Chiba-shi 260-8677, Chiba, Japan.
- 4Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoicho, Inage-ku, Chiba-shi 263-8522, Chiba, Japan.
Abstract
Background/Objectives: Monitoring and evaluating respiratory rate (RR) plays a critical role during emergency transport. Conventional RR measurement methods have primarily relied on electrocardiogram-based approaches, which are susceptible to large errors caused by patient body movement and impose a burden on patients due to their contact-based nature. This study aimed to develop a non-contact RR measurement method using RGB and thermal cameras that can be applied inside a moving ambulance. Methods: One of the key challenges in non-contact RR measurement inside an ambulance is accommodating changes in patient posture. We addressed this by patient posture-adaptive automatic image registration between RGB and thermal cameras and developing a nostril detection method robust to posture changes. Results: The image registration error of the proposed method was evaluated at various distances from the camera, and the error was confirmed to remain within 1 pixel. RR measurement experiments simulating various respiratory conditions were then conducted inside a moving ambulance. The average of mean absolute error (MAE) was 0.95, 0.59, and 2.80 breaths per minute (breaths/min) for bradypnea, normal, and tachypnea, respectively, in the supine position, and 1.08, 1.90, and 4.38 breaths/min, respectively, in the semi-sitting position. Evaluation of the proposed nostril detection method further confirmed improved RR estimation accuracy compared with the conventional method. Conclusions: This study achieved patient posture-adaptive automatic image registration and robust nostril detection and successfully conducted experiments under realistic and challenging conditions, representing a substantial step toward the real-world implementation of a non-contact RR measurement system in ambulances.
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BibTeX
@article{nemoto2026patient,
title = {{Patient Posture-Adaptive Non-Contact Respiratory Rate Measurement Inside a Moving Ambulance Using RGB and Thermal Cameras.}},
author = {Daiga Nemoto and Nozomi Takahashi and Fumitaka Mukouyama and Taka-Aki Nakada and Yukihiro Nomura and Toshiya Nakaguchi},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = oct,
doi = {10.3390/diagnostics16193244},
url = {https://europepmc.org/article/MED/42882399},
note = {CC BY},
}
RIS
TY - JOUR
TI - Patient Posture-Adaptive Non-Contact Respiratory Rate Measurement Inside a Moving Ambulance Using RGB and Thermal Cameras.
AU - Daiga Nemoto
AU - Nozomi Takahashi
AU - Fumitaka Mukouyama
AU - Taka-Aki Nakada
AU - Yukihiro Nomura
AU - Toshiya Nakaguchi
PY - 2026
DA - 2026/10/08
JO - Diagnostics (Basel, Switzerland)
DO - 10.3390/diagnostics16193244
UR - https://europepmc.org/article/MED/42882399
AB - Background/Objectives: Monitoring and evaluating respiratory rate (RR) plays a critical role during emergency transport. Conventional RR measurement methods have primarily relied on electrocardiogram-based approaches, which are susceptible to large errors caused by patient body movement and impose a burden on patients due to their contact-based nature. This study aimed to develop a non-contact RR measurement method using RGB and thermal cameras that can be applied inside a moving ambulance. Methods: One of the key challenges in non-contact RR measurement inside an ambulance is accommodating changes in patient posture. We addressed this by patient posture-adaptive automatic image registration between RGB and thermal cameras and developing a nostril detection method robust to posture changes. Results: The image registration error of the proposed method was evaluated at various distances from the camera, and the error was confirmed to remain within 1 pixel. RR measurement experiments simulating various respiratory conditions were then conducted inside a moving ambulance. The average of mean absolute error (MAE) was 0.95, 0.59, and 2.80 breaths per minute (breaths/min) for bradypnea, normal, and tachypnea, respectively, in the supine position, and 1.08, 1.90, and 4.38 breaths/min, respectively, in the semi-sitting position. Evaluation of the proposed nostril detection method further confirmed improved RR estimation accuracy compared with the conventional method. Conclusions: This study achieved patient posture-adaptive automatic image registration and robust nostril detection and successfully conducted experiments under realistic and challenging conditions, representing a substantial step toward the real-world implementation of a non-contact RR measurement system in ambulances.
SN - 2075-4418
ER -