Camera-Based Respiration Monitoring of Unconstrained Rodents.

Animal research has always been crucial for various medical and scientific breakthroughs, providing information on disease mechanisms, genetic predisposition to diseases, and pharmacological treatment. However, the use of animals in medical research is a source of great controversy and ongoing debate in modern science. To ensure a high level of bioethics, new guidelines have been adopted by the EU, implementing the 3R principles to replace animal testing wherever possible, reduce the number of animals per experiment, and refine procedures to minimize stress and pain. Supporting these guidelines, this article proposes an improved approach for unobtrusive, continuous, and automated monitoring of the respiratory rate of laboratory rats. It uses the cyclical expansion and contraction of the rats' thorax/abdominal region to determine this physiological parameter. In contrast to previous work, the focus is on unconstrained animals, which requires the algorithms to be especially robust to motion artifacts. To test the feasibility of the proposed approach, video material of multiple rats was recorded and evaluated. High agreement was obtained between RGB imaging and the reference method (respiratory rate derived from electrocardiography), which was reflected in a relative error of 5.46%. The current work shows that camera-based technologies are promising and relevant alternatives for monitoring the respiratory rate of unconstrained rats, contributing to the development of new alternatives for a continuous and objective assessment of animal welfare, and hereby guiding the way to modern and bioethical research.

Evaluation of a Camera-Based Monitoring Solution Against Regulated Medical Devices to Measure Heart Rate, Respiratory Rate, Oxygen Saturation, and Blood Pressure.

Background Regularly monitoring common physiological signs, including heart rate, blood pressure, and oxygen saturation, can effectively prevent or detect several potential conditions. In particular, cardiovascular diseases (CVDs) are a worldwide concern. According to the World Health Organization, 31% of all deaths worldwide are from CVDs. Recently, the coronavirus disease 2019 pandemic has increased the interest in remote monitoring. At present, contact devices are required to extract most of an individual's physiological information, which can be inconvenient for users and may cause discomfort. Methodology However, remote photoplethysmography (rPPG) technology offers a solution for this issue, enabling contactless monitoring of the blood volume pulse signal using a regular camera. Ultimately, it can provide the same physiological information as a contact device. In this paper, we propose an evaluation of Vastmindz's rPPG technology against medical devices in a clinical environment with a variety of subjects in a wide range of age, height, weight, and baseline vital signs. Results This study confirmed the findings that the contactless technology for the estimation of vitals proposed by Vastmindz was able to estimate heart rate, respiratory rate, and oxygen saturation with a mean error of ±3 units as well as ±10 mmHg for systolic and diastolic blood pressure. Conclusions Reported results have shown that Vastmindz's rPPG technology was able to meet the initial hypothesis and is acceptable for users who want to understand their general health and wellness.

Contactless Vital Signs Acquisition Using Video Photoplethysmography, Motion Analysis and Passive Infrared Thermography Devices During Emergency Department Walk-In Triage in Pandemic Conditions.

BACKGROUND: Contactless vital signs (VS) measurement with video photoplethysmography (vPPG), motion analysis (MA), and passive infrared thermometry (pIR) has shown promise. OBJECTIVES: To compare conventional (contact-based) and experimental contactless VS measurement approaches for emergency department (ED) walk-in triage in pandemic conditions. METHODS: Patients' heart rates (HR), respiratory rates (RR), and temperatures were measured with cardiorespiratory monitor and vPPG, manual count and MA, and contact thermometers and pIR, respectively. RESULTS: There were 475 walk-in ED patients studied (95% of eligible). Subjects were 35.2 ± 20.8 years old (range 4 days‒95 years); 52% female, 0.2% transgender; had Fitzpatrick skin type of 2.3 ± 1.4 (range 1‒6), Emergency Severity Index of 3.0 ± 0.6 (range 2‒5), and contact temperature of 36.83°C (range 35.89-39.4°C) (98.3°F [96.6‒103°F]). Pediatric HR and RR data were excluded from analysis due to research challenges associated with pandemic workflow. For a 30-s, unprimed "Triage" window in 377 adult patients, vPPG-MA acquired 377 (100%) HR measurements featuring a mean difference with cardiorespiratory monitor HR of 5.9 ± 12.8 beats/min (R = 0.6833) and 252 (66.8%) RR measurements featuring a mean difference with manual RR of -0.4 ± 2.6 beats/min (R = 0.8128). Subjects' Emergency Severity Index components based on conventional VS and contactless VS matched for 83.8% (HR) and 89.3% (RR). Filtering out vPPG-MA measurements with low algorithmic confidence reduced VS acquired while improving correlation with conventional measurements. The mean difference between contact and pIR temperatures was 0.83 ± 0.67°C (range -1.16-3.5°C) (1.5 ± 1.2°F [range -2.1-6.3°F]); pIR fever detection improved with post hoc adjustment for mean bias. CONCLUSION: Contactless VS acquisition demonstrated good agreement with contact methods during adult walk-in ED patient triage in pandemic conditions; clinical applications will need further study.

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

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.

Fast body part segmentation and tracking of neonatal video data using deep learning.

Photoplethysmography imaging (PPGI) for non-contact monitoring of preterm infants in the neonatal intensive care unit (NICU) is a promising technology, as it could reduce medical adhesive-related skin injuries and associated complications. For practical implementations of PPGI, a region of interest has to be detected automatically in real time. As the neonates' body proportions differ significantly from adults, existing approaches may not be used in a straightforward way, and color-based skin detection requires RGB data, thus prohibiting the use of less-intrusive near-infrared (NIR) acquisition. In this paper, we present a deep learning-based method for segmentation of neonatal video data. We augmented an existing encoder-decoder semantic segmentation method with a modified version of the ResNet-50 encoder. This reduced the computational time by a factor of 7.5, so that 30 frames per second can be processed at 960 × 576 pixels. The method was developed and optimized on publicly available databases with segmentation data from adults. For evaluation, a comprehensive dataset consisting of RGB and NIR video recordings from 29 neonates with various skin tones recorded in two NICUs in Germany and India was used. From all recordings, 643 frames were manually segmented. After pre-training the model on the public adult data, parts of the neonatal data were used for additional learning and left-out neonates are used for cross-validated evaluation. On the RGB data, the head is segmented well (82% intersection over union, 88% accuracy), and performance is comparable with those achieved on large, public, non-neonatal datasets. On the other hand, performance on the NIR data was inferior. By employing data augmentation to generate additional virtual NIR data for training, results could be improved and the head could be segmented with 62% intersection over union and 65% accuracy. The method is in theory capable of performing segmentation in real time and thus it may provide a useful tool for future PPGI applications. Graphical Abstract This work presents the development of a customized, real-time capable Deep Learning architecture for segmenting of neonatal videos recorded in the intensive care unit. In addition to hand-annotated data, transfer learning is exploited to improve performance.