{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T17:19:22Z","timestamp":1792084762346,"version":"4.4.0"},"reference-count":13,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009619","name":"AMED","doi-asserted-by":"publisher","award":["JP256f0137011"],"award-info":[{"award-number":["JP256f0137011"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"award":["JP256f0137011"],"award-info":[{"award-number":["JP256f0137011"]}],"id":[{"id":"https:\/\/ror.org\/004rtk039","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Computed tomography (CT) examinations pose challenges for continuous patient observation, particularly when adverse events such as severe reactions to contrast media occur. To improve patient monitoring in such situations, this preliminary study proposes a contactless method for respiratory waveform estimation using a depth camera and AI-based body detection. The method identifies anatomically relevant respiratory regions and extracts depth-based motion signals while subjects are seated facing the camera, which is positioned approximately 2 m away. Performance was evaluated experimentally using a wearable force-sensor respiration belt as the reference. Quantitative assessment was conducted using waveform error metrics, Pearson correlation coefficients, respiratory-rate agreement, and Bland\u2013Altman analysis, while qualitative analysis was used to examine the influence of clothing conditions on measurement performance. The results show that the proposed method can provide stable respiratory waveform estimation, with the chest region yielding the lowest waveform error and the highest correlation among the evaluated ROIs. Bland\u2013Altman analysis further indicated small systematic errors in respiratory-rate estimation, although variability-related indices were affected by ROI selection and clothing conditions. These findings support the feasibility of the proposed approach for contactless respiratory monitoring during CT examinations and indicate that the main contribution of this study is to clarify the importance of anatomical ROI selection for robust waveform extraction under CT-oriented monitoring conditions.<\/jats:p>","DOI":"10.3390\/s26113476","type":"journal-article","created":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:11:01Z","timestamp":1780333861000},"page":"3476","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Contactless Respiratory Waveform Estimation Using a Depth Camera and AI-Based Body Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0719-5934","authenticated-orcid":false,"given":"Yuto","family":"Kojima","sequence":"first","affiliation":[{"name":"Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City 739-8527, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0631-7271","authenticated-orcid":false,"given":"Toru","family":"Higaki","sequence":"additional","affiliation":[{"name":"Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City 739-8527, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4221-4060","authenticated-orcid":false,"given":"Hirotaka","family":"Inoue","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Information Science, National Institute of Technology (KOSEN), Kure College, 2-2-11 Agaminami, Kure City 737-8506, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2146-415X","authenticated-orcid":false,"given":"Bisser","family":"Raytchev","sequence":"additional","affiliation":[{"name":"Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City 739-8527, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanlei","family":"Gu","sequence":"additional","affiliation":[{"name":"Graduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima City 739-8527, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3128-4731","authenticated-orcid":false,"given":"Yuko","family":"Nakamura","sequence":"additional","affiliation":[{"name":"Department of Diagnostic Radiology, Kasumi Campus, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima City 734-8551, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kojima, Y., Higaki, T., and Inoue, H. (2025). Breath Waveform Measurement System Using Contact-less Devices. 2025 IEEE 13th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), IEEE.","DOI":"10.1109\/IDAACS68557.2025.11322010"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Addison, A.P., Addison, P.S., Smit, P., Jacquel, D., and Borg, U.R. (2021). Non-Contact Respiratory Monitoring Using Depth Sensing Cameras: A Review of Current Literature. Sensors, 21.","DOI":"10.3390\/s21041135"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2046","DOI":"10.1109\/TMTT.2013.2256924","article-title":"A Review on Recent Advances in Doppler Radar Sensors for Noncontact Healthcare Monitoring","volume":"61","author":"Li","year":"2013","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1007\/s10877-025-01319-6","article-title":"Non-Contact (Touchless) Monitoring of Respiratory Rate in a Challenging Anesthesia Setting Using a Depth Camera","volume":"40","author":"MacLeod","year":"2025","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Proch\u00e1zka, A., Sch\u00e4tz, M., Vy\u0161ata, O., and Vali\u0161, M. (2016). Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis. Sensors, 16.","DOI":"10.3390\/s16070996"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.1007\/s10877-023-01003-7","article-title":"Robust Non-Contact Monitoring of Respiratory Rate Using a Depth Camera","volume":"37","author":"Addison","year":"2023","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1002\/acm2.12318","article-title":"Comparative Analysis of Respiratory Motion Tracking Using the Microsoft Kinect v2 Sensor","volume":"19","author":"Silverstein","year":"2018","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s10877-021-00691-3","article-title":"Continuous Non-Contact Respiratory Rate and Tidal Volume Monitoring Using a Depth Sensing Camera","volume":"36","author":"Addison","year":"2022","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Imano, W., Kameyama, K., Hollingdal, M., Refsgaard, J., Larsen, K., Topp, C., Kronborg, S.H., Gade, J.D., and Dinesen, B. (2020). Non-Contact Respiratory Measurement Using a Depth Camera for Elderly People. Sensors, 20.","DOI":"10.3390\/s20236901"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Chavernac, F., Albert, K., Huy, H.V., Ramachandran, S., Noumeir, R., and Jouvet, P. (2025). Real-Time Current Volume Estimation System from an Azure Kinect Camera in Pediatric Intensive Care: Technical Development. Sensors, 25.","DOI":"10.3390\/s25103069"},{"key":"ref_11","unstructured":"Microsoft (2026, March 19). Azure Kinect DK Documentation. Available online: https:\/\/learn.microsoft.com\/en-us\/previous-versions\/azure\/kinect-dk\/."},{"key":"ref_12","unstructured":"Microsoft (2026, March 19). Azure Kinect Body Tracking SDK Documentation. Available online: https:\/\/learn.microsoft.com\/en-us\/previous-versions\/azure\/kinect-dk\/body-sdk-download."},{"key":"ref_13","unstructured":"Liu, Z. (2026, May 15). 3D Skeletal Tracking on Azure Kinect\u2014Azure Kinect Body Tracking SDK. Microsoft Research. Available online: https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2020\/01\/AKBTSDK.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3476\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T17:14:01Z","timestamp":1780334041000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/11\/3476"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,1]]},"references-count":13,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26113476"],"URL":"https:\/\/doi.org\/10.3390\/s26113476","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,1]]}}}