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The Pediatric Intensive Care Unit (PICU) receives critically ill patients with shortness of breath and poor body oxygenation. Various respiratory parameters, such as respiratory rate, oxygen saturation level, and heart rate, are continuously monitored to timely adapt their management. With the advancement in technology, measurements of most parameters are carried out by medical instruments. However, some crucial parameters are still measured via visual examination, particularly the assessment of chest deformation, which is vital in assessing acute respiratory distress (ARD) conditions. However, visual examination is subjective and intermittent, prone to human error, and challenging to monitor patients round the clock. This subjectivity becomes problematic, especially in areas with a shortage of specialists, such as remote locations, developing countries, or during pandemics. In this paper, we propose an automated acute respiratory distress condition detection system, to address challenges associated with visual examination. The proposed approach utilizes a high-definition camera to capture patient temporal visual information and employs advanced deep-learning models to detect ARD condition. In order to test the feasibility, we collected video data of 153 patients, including both with and without ARD in the PICU. As the deep learning models require substantial amounts of data, and collecting data in the medical domain, particularly in the PICU, poses challenges. To overcome data limited problem, we utilized the problem-specific information, opted transfer learning and data augmentation techniques. Additionally, we compute baseline results of various video analysis algorithms for ARD detection task. Experimental results illustrate that the deep learning base video analysis algorithms have the potential to automate the visual examination process for the ARD detection task, by achieving an accuracy of 0.82, precision of 0.80, recall of 0.89, and $F_{1}$ score of 0.84.

This study aimed to investigate the application of label propagation techniques to propagate labels among photoplethysmogram (PPG) signals, particularly in imbalanced class scenarios and limited data availability scenarios, where clean PPG samples are significantly outnumbered by artifact-contaminated samples. We investigated a dataset comprising PPG recordings from 1571 patients, wherein approximately 82% of the samples were identified as clean, while the remaining 18% were contaminated by artifacts. Our research compares the performance of supervised classifiers, such as conventional classifiers and neural networks (Multi-Layer Perceptron (MLP), Transformers, Fully Convolutional Network (FCN)), with the semi-supervised Label Propagation (LP) algorithm for artifact classification in PPG signals. The results indicate that the LP algorithm achieves a precision of 91%, a recall of 90%, and an F1 score of 90% for the “artifacts” class, showcasing its effectiveness in annotating a medical dataset, even in cases where clean samples are rare. Although the K-Nearest Neighbors (KNN) supervised model demonstrated good results with a precision of 89%, a recall of 95%, and an F1 score of 92%, the semi-supervised algorithm excels in artifact detection. In the case of imbalanced and limited pediatric intensive care environment data, the semi-supervised LP algorithm is promising for artifact detection in PPG signals. The results of this study are important for improving the accuracy of PPG-based health monitoring, particularly in situations in which motion artifacts pose challenges to data interpretation.

Falls are a growing issue in society and has become a hot topic in the healthcare domain. Falls are more likely to occur to due to age or health problems such as cardiovascular issues and muscle weakness. In this work we focus on fall detection. The aftereffects of falls often lead to the use of prescription pain medications. We are motivated to help prevent suicide attempts by overdose in the Canadian correctional services. Most previous studies were based on hand-crafted features which limit the robustness and generality of the system. We therefore propose a general vision-based system, using Spatial Temporal Graph Convolutional Networks (ST-GCN). This system has proven its efficiency and robustness in the action recognition domain. Contrary to previous works, this model can be applied directly to new data without the need to retrain the model while offering good accuracy. Additionally, with the help of transfer learning we can solve the insufficient data problem. By using three public datasets: the NTU RGB-D dataset, the TST Fall detection dataset v2 and the Fallfree dataset to validate our method, we achieved a 100% accuracy, surpassing the state-of-the-art.

For skeleton-based action recognition from depth cameras, distinguishing object-related actions with similar motions is a difficult task. The other available video streams (RGB, infrared, depth) may provide additional clues, given an appropriate feature fusion strategy. We propose a modular network combining skeleton and infrared data. A pre-trained 2D convolutional neural network (CNN) is used as a pose module to extract features from skeleton data. A pre-trained 3D CNN is used as an infrared module to extract visual features from videos. Both feature vectors are then fused and exploited jointly using a multilayer perceptron (MLP). The 2D skeleton coordinates are used to crop a region of interest around the subjects for the infrared videos. Infrared is favored over RGB, as it is less affected by illumination conditions and usable in the dark. We are the first to combine infrared and skeleton data. We evaluate our method on the NTU RGB+D dataset, the largest dataset for human action recognition from depth cameras. We perform extensive ablation studies. In particular, we show the strong contributions of our cropping strategy and pre-training on action classification accuracy. We also test various feature fusion schemes. Feature sum on an element-wise level yields the best results. Our method achieves state-of-the-art performances on NTU RBG+D.

In this paper, we propose a solution for video transmission over a low-bandwidth network that enables a physician to take in charge of remote trauma patient. We propose and analyze a method based on an H.264 compression scheme that relies on transmitting high-quality video of a moving and dynamic region of interest while scarifying quality in the background. Our method is motivated by the problem of limited bandwidth usually encountered in air-to-ground communication channels. We propose to use a region of interest with smoothed edges to increase the video quality of the transition between the regions of various qualities. The moving region of interest, covering the torso and the head, is segmented and tracked by using the skeleton information provided by a Kinect camera. Our proposed compression scheme respects the real-time, low-complexity, and interoperability constraints. We have analyzed the results of our method obtained with various bit rate targets and have shown that a visual assessment of a patient is achievable over very low bandwidth.

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).

After three decades from the public debut of cellular networks, there are hardly parts of populated lands where cellular coverage is absent. Day after day, mobile users have been provided with wider range of services at higher speed. Today, long terminal evolution (LTE) networks support broadband connectivity for users moving as fast as 350km/h, and the support for speeds up to 500km/h is under consideration. Unfortunately, none of these efforts were aimed at airborne travelers due to the lack of aerial coverage. Provided that 5G networks are meant to provide anywhere and anytime connectivity for anyone, many operators are providing the free onboard Wi-Fi through proprietary terrestrial networks or satellite links. Unfortunately, both of these solutions have serious drawbacks, where the latter provides very limited speed, and the former is expensive and unscalable. In this paper, we discuss the technical possibilities of enhancing the existing LTE infrastructure for air to ground communications. We identify the major challenges and obstacles in this path, such as uplink/downlink interferences, frequent roaming, large Doppler effect, and channel degradation. We also discuss appropriate solutions to counteract them using some of the emerging antenna, signal processing, beamforming, and multi-beaming ideas.