ArticleSensors (Basel, Switzerland)2019
Non-Contact Monitoring of Breathing Pattern and Respiratory Rate via RGB Signal Measurement.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers, 2 of them syntheses that pooled it.
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Who cites it
36 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Continuous Monitoring of Vital Signs Using Cameras: A Systematic Review.Sensors (Basel, Switzerland) · 2022Pooled it
- Sensing Systems for Respiration Monitoring: A Technical Systematic Review.Sensors (Basel, Switzerland) · 2020Pooled it
- Cardio-respiratory deep learning model to predict hospital admission from emergency department electrocardiogram images and respiration videos: a prospective study.BioData mining · 2026Article
- Automatic annotation to train ROI detection algorithm for premature infant respiration monitoring in NICU.Medical & biological engineering & computing · 2026Article
- Reconstruction of Respiratory Flow from an Impedance-Based Contactless Sensor System.Sensors (Basel, Switzerland) · 2025Article
- PICU Face and Thoracoabdominal Detection Using Self-Supervised Divided Space-Time Mamba.Life (Basel, Switzerland) · 2025Article
- Estimating Sleep-Stage Distribution from Respiratory Sounds via Deep Audio Segmentation.Sensors (Basel, Switzerland) · 2025Article
- Contactless Vital Sign Monitoring: A Review Towards Multi-Modal Multi-Task Approaches.Sensors (Basel, Switzerland) · 2025Review
- Article
- For a clinical application of optical triangulation to assess respiratory rate using an RGB camera and a line laser.BMC medical imaging · 2024Article
- Is breathing frequency a potential means for monitoring exercise intensity in people with atrial fibrillation and coronary heart disease when heart rate is mitigated?European journal of applied physiology · 2024Observational
- Two-Stream Convolutional Neural Networks for Breathing Pattern Classification: Real-Time Monitoring of Respiratory Disease Patients.Bioengineering (Basel, Switzerland) · 2024Article
- Breathing Chest Wall Kinematics Assessment through a Single Digital Camera: A Feasibility Study.Sensors (Basel, Switzerland) · 2023Article
- COVID-19 symptom identification using Deep Learning and hardware emulated systems.Engineering applications of artificial intelligence · 2023Article
- Non-Contact Monitoring of Fetal Movement Using Abdominal Video Recording.Sensors (Basel, Switzerland) · 2023Article
- Multi-Modal Portable Respiratory Rate Monitoring Device for Childhood Pneumonia Detection.Micromachines · 2023Article
- Breathing-Associated Facial Region Segmentation for Thermal Camera-Based Indirect Breathing Monitoring.IEEE journal of translational engineering in health and medicine · 2023Article
- Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare.Journal of healthcare engineering · 2023Article
- Machine-Learning Classification of Pulse Waveform Quality.Sensors (Basel, Switzerland) · 2022Article
- Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time.IEEE transactions on biomedical circuits and systems · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Among all the vital signs, respiratory rate remains the least measured in several scenarios, mainly due to the intrusiveness of the sensors usually adopted. For this reason, all contactless monitoring systems are gaining increasing attention in this field. In this paper, we present a measuring system for contactless measurement of the respiratory pattern and the extraction of breath-by-breath respiratory rate. The system consists of a laptop's built-in RGB camera and an algorithm for post-processing of acquired video data. From the recording of the chest movements of a subject, the analysis of the pixel intensity changes yields a waveform indicating respiratory pattern. The proposed system has been tested on 12 volunteers, both males and females seated in front of the webcam, wearing both slim-fit and loose-fit t-shirts. The pressure-drop signal recorded at the level of nostrils with a head-mounted wearable device was used as reference respiratory pattern. The two methods have been compared in terms of mean of absolute error, standard error, and percentage error. Additionally, a Bland-Altman plot was used to investigate the bias between methods. Results show the ability of the system to record accurate values of respiratory rate, with both slim-fit and loose-fit clothing. The measuring system shows better performance on females. Bland-Altman analysis showed a bias of -0.01 breaths · min - 1 , with respiratory rate values between 10 and 43 breaths · min - 1 . Promising performance has been found in the preliminary tests simulating tachypnea.
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