ArticleJournal of clinical monitoring and computing2026
Non-contact (touchless) monitoring of respiratory rate in a challenging anesthesia setting using a depth camera.
Article in Journal of clinical monitoring and computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Contactless Respiratory Waveform Estimation Using a Depth Camera and AI-Based Body Detection.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
aimWe have developed a non-contact ("touchless") system based on depth-sensing camera technology for continuous monitoring of respiratory activity. Previous work from our group has demonstrated high accuracy of the system in monitoring a wide range of respiratory rates and signal morphologies across diverse conditions, including variations in lighting, posture, and coverings. Here, we report on the system's performance in a significantly more challenging anesthesia environment which included a wide range of respiratory rates and respiratory patterns, spontaneous and hand ventilated breathing, patient motion and caregiver interactions in the scene, and, in some cases, the presence of warming blankets covering the torso.
methodsData was collected opportunistically from 34 healthy volunteers from two separate studies, both of which had the primary objective of investigating the relationship between depth of anesthesia monitoring and anesthetic agents (inhaled and intravenous) across a wide range of anesthetic concentrations and hypnotic states. Depth-sensing information was acquired using an Intel D415 RealSense™ camera and processed to extract frame-by-frame depth changes within the subject's torso region corresponding to respiratory activity. A respiratory rate (RR
resultsThe overall RMSD accuracy [bias] obtained for the combined data set was 1.92 [0.30] breaths/min. The performance results stratified according to pre-, intra-, and post-anesthesia stages were 1.71 [0.15], 1.95 [0.39] and 2.13 [0.08] breaths/min, respectively.
conclusionsWe have demonstrated the ability to continuously track respiratory rate during challenging conditions within an anesthesia setting using our non-contact, touchless, monitoring technology. We believe that our findings support the potential utility for continuous non-contact monitoring of respiration in clinical areas, such as the post-anesthesia care environment.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.