Assessment of respiratory activity in pediatric intensive care unit allows a comprehensive view of the patient's condition. This allows the identification of high-risk cases for prompt and appropriate medical treatment. Numerous research works on respiration monitoring have been conducted in recent years. However, most of them are unsuitable for clinical environment or require physical contact with the patient, which limits their efficiency. In this paper, we present a novel system for measuring the breathing pattern based on a computer vision method and contactless design. Our 3D imaging system is specifically designed for pediatric intensive care environment, which distinguishes it from the other imaging methods. Indeed, previous works are mostly limited to the use of conventional video acquisition devices, in addition to not considering the constraints imposed by intensive care environment. The proposed system uses depth information captured by two (Red Green Blue-Depth) RGB-D cameras at different view angles, by considering the intensive care unit constraints. Depth information is then exploited to reconstruct a 3D surface of a patient's torso with high temporal and spatial resolution and large spatial coverage. Our system captures the motion information for the top of the torso surface as well as for its both lateral sides. For each reconstruction, the volume is estimated through a recursive subdivision of the 3D space into cubic unit elements. The volume change is then calculated through a subtraction technique between successive reconstructions. We tested our system in the pediatric intensive care unit of the Sainte-Justine university hospital center, where it was compared to the gold standard method currently used in pediatric intensive care units. The performed experiments showed a very high accuracy and precision of the proposed imaging system in estimating respiratory rate and tidal volume.

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This paper presents a novel computer vision method to measure the breathing pattern in intensive care environment. The proposed system uses depth information captured by two RGB-D cameras in order to reconstruct a 3D surface of a patient's torso with a high spatial coverage. The optimal positioning for the sensors is a key step to perform an accurate 3D reconstruction without interfering with patient care. In this context, our hardware setup meets the clinical requirements while allowing accurate estimation of respiratory parameters including respiratory rate, tidal volume and inspiratory time. Our system provides the motion information not only for the top of the torso surface but also for its both lateral sides. Our method was tested in an environment designed for critically ill children, where it was compared to the gold standard method currently used in intensive care units. The performed experiments yielded high accuracy and showed significant agreement with gold standard method.

In a situation of respiratory failure (RF), patients show signs of increased work of breathing leading to the involvement of accessory respiratory muscles and a desynchronization between rib cage and abdomen known as thoraco-abdominal asynchrony (TAA). Proper assessment of these signs requires sufficiently skilled and trained medical staff. However, human assessment is subjective and is practically impossible to audit. A new non-contact method is proposed for TAA visualization and quantification, in children with RF. The surface variations are analyzed by calculating the 3-dimensional motion of the thorax and abdomen regions during the breathing process. A high-fidelity mannequin was used to simulate thoraco-abdominal asynchrony. The proposed system uses depth information recorded by an RGB-D (Red Green Blue-Depth) camera. Furthermore, surface displacement was calculated in four simulated modes from the normal to the severe TAA mode. Respiratory rates were also calculated based on the analysis of the surface movements. The proposed method was compared to a highly precise laser-ranging system with 1 mm resolution. The resulting root mean square deviation (RMSD) showed an error of 1.78 ml in normal mode, 2.83 mm in mild mode, 2.23 mm in severe mode and 2.34 mm in irregular mode. The results showed a high correlation between the two methods in estimating the retraction distance and respiratory rate (ρ >0.985).

Assessment of respiratory function allows early detection of potential disorders in the respiratory system and provides useful information for medical management. There is a wide range of applications for breathing assessment, from measurement systems in a clinical environment to applications involving athletes. Many studies on pulmonary function testing systems and breath monitoring have been conducted over the past few decades, and their results have the potential to broadly impact clinical practice. However, most of these works require physical contact with the patient to produce accurate and reliable measures of the respiratory function. There is still a significant shortcoming of non-contact measuring systems in their ability to fit into the clinical environment. The purpose of this paper is to provide a review of the current advances and systems in respiratory function assessment, particularly camera-based systems. A classification of the applicable research works is presented according to their techniques and recorded/quantified respiration parameters. In addition, the current solutions are discussed with regards to their direct applicability in different settings, such as clinical or home settings, highlighting their specific strengths and limitations in the different environments.