{"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":1792084762310,"version":"4.4.0"},"reference-count":57,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Indonesia Endowment Fund for Education (LPDP), Ministry of Finance of the Republic of Indonesia","award":["LOG-13094\/LPDP.3\/2024"],"award-info":[{"award-number":["LOG-13094\/LPDP.3\/2024"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>(1) Background: This study presents an adaptive, contactless, and privacy-preserving respiratory-rate monitoring system based on thermal imaging, designed for real-time operation on embedded edge hardware. The system continuously processes temperature data from a compact thermal camera without external computation, enabling practical deployment for home or clinical vital-sign monitoring. (2) Methods: Thermal frames are captured using a 256\u00d7192 TOPDON TC001 camera and processed entirely on an NVIDIA Jetson Orin Nano. A YOLO-based detector localizes the nostril region in every even frame (stride = 2) to reduce the computation load, while a Kalman filter predicts the ROI position on skipped frames to maintain spatial continuity and suppress motion jitter. From the stabilized ROI, a temperature-based breathing signal is extracted and analyzed through an adaptive median\u2013MAD hysteresis algorithm that dynamically adjusts to signal amplitude and noise variations for breathing phase detection. Respiratory rate (RR) is computed from inter-breath intervals (IBI) validated within physiological constraints. (3) Results: Ten healthy subjects participated in six experimental conditions including resting, paced breathing, speech, off-axis yaw, posture (supine), and distance variations up to 2.0 m. Across these conditions, the system attained a MAE of 0.57\u00b10.36 BPM and an RMSE of 0.64\u00b10.42 BPM, demonstrating stable accuracy under motion and thermal drift. Compared with peak-based and FFT spectral baselines, the proposed method reduced errors by a large margin across all conditions. (4) Conclusions: The findings confirm that accurate and robust respiratory-rate estimation can be achieved using a low-resolution thermal sensor running entirely on an embedded edge device. The combination of YOLO-based nostril detector, Kalman ROI prediction, and adaptive MAD\u2013hysteresis phase that self-adjusts to signal variability provides a compact, efficient, and privacy-preserving solution for non-invasive vital-sign monitoring in real-world environments.<\/jats:p>","DOI":"10.3390\/s26010278","type":"journal-article","created":{"date-parts":[[2026,1,2]],"date-time":"2026-01-02T09:56:23Z","timestamp":1767347783000},"page":"278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Adaptive Thermal Imaging Signal Analysis for Real-Time Non-Invasive Respiratory Rate Monitoring"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9614-6386","authenticated-orcid":false,"given":"Riska","family":"Analia","sequence":"first","affiliation":[{"name":"School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9LN, UK"},{"name":"Department of Electrical Engineering, Politeknik Negeri Batam, Batam 29461, Indonesia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7466-4414","authenticated-orcid":false,"given":"Anne","family":"Forster","sequence":"additional","affiliation":[{"name":"Academic Unit for Ageing and Stroke Research, Leeds Institute of Health Sciences, University of Leeds, Leeds LS2 9JT, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2641-2620","authenticated-orcid":false,"given":"Sheng-Quan","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9LN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0204-3867","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic and Electrical Engineering, University of Leeds, Leeds LS2 9LN, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"00023","DOI":"10.1183\/23120541.00023-2020","article-title":"Current clinical methods of measurement of respiratory rate give imprecise values","volume":"6","author":"Drummond","year":"2020","journal-title":"ERJ Open Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/BF01709831","article-title":"Breathing pattern analysis","volume":"18","author":"Tobin","year":"2005","journal-title":"Intensive Care Med."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"27794","DOI":"10.1038\/s41598-024-77778-9","article-title":"Kinematic signature of high risk labored breathing revealed by novel signal analysis","volume":"14","author":"Ashe","year":"2024","journal-title":"Sci. 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