Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Rehouma, Haythem; Noumeir, Rita; Essouri, Sandrine; Jouvet, Philippe

doi:10.3390/s20247252

Cited by 17 publications

(12 citation statements)

References 246 publications

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“…With the advances in cameras technology and computer vision, researchers developed different methods for assessing respiratory activity based on cheap and compact RBG-D sensors [28] ( Table 3 ). One approach, suitable for gating applications, uses markers [29] or amplification surfaces [16] .…”

Section: Discussionmentioning

confidence: 99%

Contactless Monitoring of Breathing Pattern and Thoracoabdominal Asynchronies in Preterm Infants Using Depth Cameras: A Feasibility Study

Ottaviani

Veneroni

Dellacà

et al. 2022

IEEE J. Transl. Eng. Health Med.

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Objective: Monitoring infants’ breathing activity is crucial in research and clinical applications but remains a challenge. This study aims to develop a contactless method to monitor breathing patterns and thoracoabdominal asynchronies in infants inside the incubator, using depth cameras. Methods: We proposed an algorithm to extract the 3D displacements of the ribcage and abdomen from the analysis of depth images. We evaluated the accuracy of the system in-vitro vs. a reference motion capture analyzer. We also conducted a feasibility study on 12 patients receiving non-invasive respiratory support to estimate the mean and the variability of the chest wall displacements in preterm infants and evaluate the suitability of the proposed system in the clinical setting. Results: In-vitro , the mean (95% CI) error in the measurement of amplitude, frequency and phase shift between compartmental displacements was −0.14 (−0.57, 0.28) mm, 0.02 (−0.99, 1.03) bpm, and −0.40 (−1.76, 0.95)°, respectively. In-vivo , the mean (95% CI) amplitude of the ribcage and abdomen displacements were 0.99 (0.34, 2.67) mm and 1.20 (0.40, 2.15) mm, respectively. Conclusions: The developed system proved accurate in-vitro and was suitable for the clinical environment. Clinical Impact: The proposed method has value for evaluating infants’ breathing patterns in research applications and, after further development, may represent a simple monitoring tool for infants’ respiratory activity inside the incubator.

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Section: Discussionmentioning

confidence: 99%

Contactless Monitoring of Breathing Pattern and Thoracoabdominal Asynchronies in Preterm Infants Using Depth Cameras: A Feasibility Study

Ottaviani

Veneroni

Dellacà

et al. 2022

IEEE J. Transl. Eng. Health Med.

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“…4 However, both technologies might easily disturb the patient and the latter might alter their respiratory activities from a clinical perspective due to their contact-based nature. 5 Furthermore, contact-free ones are not to be forgotten easily or lost contact because they do not need to be applied to the patient. Bartula et al 6 proposed a correlation-based algorithm to derive subtle respiratory movement from video recordings of a single monochrome camera.…”

Section: Introductionmentioning

confidence: 99%

Respiration extraction and atrial fibrillation detection from clinical data based on single RGB camera

Que

Esch

Cramer

et al. 2023

Medical Imaging 2023: Imaging Informatics for Healthcare, Research, and Applications

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In this work, we investigated the feasibility of extracting continuous respiratory parameters from a single RGB camera stationed in a short-stay ward. Based on the extracted respiration parameters, we further investigated the feasibility of using respiratory features to aid in the detection of atrial fibrillation (AF). To extract respiration, we implemented two algorithms: chest optical flow (COF) and energy variance maximization (EVM). We used COF to extract respiration from the patient’s thoracic area and EVM from the patient’s facial area. Using capnography as the reference, for average breath-to-breath rate estimation (i.e., 15-second sliding windows with 50% overlap), we achieved errors within 3 breaths per minute with COF and within 3.5 breaths per minute with EVM. To detect the presence of AF in the respiratory signal, we extracted three respiratory features from the derived COF measurements. We fed these features to a logistic regression model and achieved an average AUC value of 0.64. This result showcases the potential of using camera-based respiratory parameters as predictors for AF, or as surrogate predictors when there is no sufficient facial area in the camera’s field of view for the extraction of cardiac measurements.

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“…Not aiming at a comprehensive literature review, we mention a couple of recent studies in this domain before establishing the research objective of this paper. A review of current advances and systems in contactless optical imaging based respiratory assessment is given in (Rehouma et al 2020), where strengths and limitations of different imaging modalities (e.g. depth senor, CCD camera, radar, LiDAR, stereoscopy) were highlighted in different environments and settings.…”

Section: Introductionmentioning

confidence: 99%

Algorithmic insights of camera-based respiratory motion extraction

Wang

Brinker

2022

Physiol. Meas.

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Objective: Measuring the respiratory signal from a video based on body motion has been proposed and recently matured in products for contactless health monitoring. The core algorithm for this application is the measurement of tiny chest/abdominal motions induced by respiration (i.e. capturing sub-pixel displacement caused by subtle motion between subsequent video frames), and the fundamental challenge is motion sensitivity. Though prior art reported on the validation with real human subjects, there is no thorough or rigorous benchmark to quantify the sensitivities and boundary conditions of motion-based core respiratory algorithms. Approach: A set-up was designed with a fully-controllable physical phantom to investigate the essence of core algorithms, together with a mathematical model incorporating two motion estimation strategies and three spatial representations, leading to six algorithmic combinations for respiratory signal extraction. Their promises and limitations are discussed and clarified through the phantom benchmark. Main results: With the variation of phantom motion intensity between 0.5 mm and 8 mm, the recommended approach obtains an average precision, recall, coverage and MAE of 88.1%, 91.8%, 95.5% and 2.1bpm in the day-light condition, and 81.7%, 90.0%, 93.9% and 4.4 bpm in the night condition. Significance: The insights gained in this paper are intended to improve the understanding and applications of camera-based respiration measurement in health monitoring. The limitations of this study stem from the used physical phantom that does not consider human factors like body shape, sleeping posture, respiratory diseases, etc., and the investigated scenario is focused on sleep monitoring, not including scenarios with a sitting or standing patient like in clinical ward and triage.

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Advancements in Methods and Camera-Based Sensors for the Quantification of Respiration

Cited by 17 publications

References 246 publications

Contactless Monitoring of Breathing Pattern and Thoracoabdominal Asynchronies in Preterm Infants Using Depth Cameras: A Feasibility Study

Contactless Monitoring of Breathing Pattern and Thoracoabdominal Asynchronies in Preterm Infants Using Depth Cameras: A Feasibility Study

Respiration extraction and atrial fibrillation detection from clinical data based on single RGB camera

Algorithmic insights of camera-based respiratory motion extraction

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