ArticleJournal of clinical monitoring and computing2023
Robust Non-Contact Monitoring of Respiratory Rate using a Depth Camera.
Article in Journal of clinical monitoring and computing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Contactless Respiratory Waveform Estimation Using a Depth Camera and AI-Based Body Detection.Sensors (Basel, Switzerland) · 2026Article
- Non-contact (touchless) monitoring of respiratory rate in a challenging anesthesia setting using a depth camera.Journal of clinical monitoring and computing · 2026Article
- Touchless monitoring of neonatal activity: a multi-center study.Pediatric research · 2026Article
- Real-Time Current Volume Estimation System from an Azure Kinect Camera in Pediatric Intensive Care: Technical Development.Sensors (Basel, Switzerland) · 2025Article
- Noncontact respiratory monitoring during sleep: comparison of the touchless flow signal with respiratory inductance plethysmography flow (RIPJournal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Non-Contact Monitoring of Inhalation-Exhalation (I:E) Ratio in Non-Ventilated Subjects.IEEE journal of translational engineering in health and medicine · 2024Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeRespiratory 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.
methodsSeven 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
resultsAn 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.
conclusionsWe 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.
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