Can video streaming improve first aid for injured patients? A prospective observational study from Norway.

BACKGROUND: Video streaming in emergency medical communication centers (EMCC) from caller to medical dispatcher has recently been introduced in some countries. Death by trauma is a leading cause of death and injuries are a frequent reason to contact EMCC. We aimed to investigate if video streaming is associated with recognition of a need for first aid during calls regarding injured patients and improve quality of bystander first aid. METHODS: A prospective observational study including patients from three health regions in Norway, from November 2021 to February 2023 (registered in clinical trials 10/25/2021, NCT05121649). Cases where video streaming had been used as a supplement during the medical emergency call were compared to cases where video streaming was not used during the call. Patients were included by ambulance personnel on the scene of accident if they met the following criteria: 1. Ambulance personnel arrived at a patient who had an injury, 2. One or more bystanders had been present before their arrival, 3. One or more of the following first aid measures had been performed by bystander or should have been performed: airway management, control of external bleeding, recovery position, and hypothermia prevention. Ambulance personnel assessed quality of first aid performed by bystander, and information concerning use of video streaming and patient need for first aid measures recognized by dispatcher was collected through EMCC audio logs and patient charts. We present descriptive data and results from a logistic regression analysis. RESULTS: Data was collected on 113 cases, and dispatchers used video streaming in addition to standard telephone communication in 12/113 (10%) of the cases. The odds for the dispatcher to recognize a need for first aid during a medical emergency call were more than five times higher when video streaming was used compared to no use of video streaming (OR 5.30, 95% CI 1.11-25.44). Overall quality of bystander first aid was rated as "high". The odds ratio for the patient receiving first aid of higher quality were 1.82 (p-value 0.46) when video streaming was used by dispatcher during the call. CONCLUSION: Our findings show that video streaming is not frequently used by dispatchers in calls regarding patients with injuries, but that video streaming is associated with improved recognition of patients' first aid needs. We found no statistically significant difference in first aid quality comparing the calls where video streaming as a supplement were used with the calls with audio only.

An automated ICU agitation monitoring system for video streaming using deep learning classification.

OBJECTIVE: To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning. METHODS: We collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation as "Attention" and "Non-attention". After transforming the video segments into movement quantification, we constructed the models of agitation classifiers with Threshold, Random Forest, and LSTM and evaluated their performances. RESULTS: The video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction. The LSTM model achieved remarkable accuracy (ACC 0.92, AUC 0.91), outperforming other methods. CONCLUSION: Our study proposes an advanced monitoring system combining LSTM and image processing to ensure mild patient sedation in ICU care. LSTM proves to be the optimal choice for accurate monitoring. Future efforts should prioritize expanding data collection and enhancing system integration for practical application.

Resource-aware video streaming (RAViS) framework for object detection system using deep learning algorithm.

Video streams can come from various sources, such as surveillance cameras, live events, drones, and video-sharing platforms. Video stream mining is challenging due to the extensive resources needed to analyze and extract useful information from continuous video data streams. This situation could result in overwhelmed resources, which causes the system to stall. One of the ways to suffice the requirement is to provide larger resources, which leads to more costs. This research develops a data stream mining called the Resource-Aware Video Streaming (RAViS) framework to adapt to the limited resources (a Raspberry Pi) to run an object detection system using the YOLO algorithm. We validate the framework by capturing video streaming to simulate data streams. The video frames are processed using a deep-learning model to recognize the presence of a person(s) in a room. The RAViS framework adapts the object detection system to the availability of Raspberry Pi resources, such as CPU, RAM, and internal storage. The adaptation aims to increase the availability of resources to perform object detection of streamed video. The experimental results indicate that the RAViS framework can adapt the detection system to resource availability while maintaining accuracy. •A framework can ensure the availability of a computer with limited resources for running an object detection system using deep learning algorithms.•The framework constantly monitors the computer's memory, CPU, and storage, and provides feedback to the object detection system for adjusting its parameters to optimize resource utilization.•This approach enables the object detection system to operate continuously with the required resources, thus ensuring its accuracy and effectiveness.

Optimal Resource Allocation for Loss-Tolerant Multicast Video Streaming.

In video streaming applications, especially during live streaming events, video traffic can account for a significant portion of the network traffic and can lead to severe network congestion. For such applications, multicast provides an efficient means to deliver the same content to a large number of users simultaneously. However, in multicast, if the base station transmits content at rates higher than what can be decoded by users with the worst channels, these users will experience outages. This makes the multicast system's performance dependent on the weakest users in the system. Interestingly, video streams can tolerate some packet loss without a significant degradation in the quality experienced by the users. This property can be leveraged to improve the multicast system's performance by reducing the dependence of the multicast transmissions on the weakest users. In this work, we design a loss-tolerant video multicasting system that allows for some controlled packet loss while satisfying the quality requirements of the users. In particular, we solve the resource allocation problem in a multimedia broadcast multicast services (MBMS) system by transforming it into the problem of stabilizing a virtual queuing system. We propose two loss-optimal policies and demonstrate their effectiveness using numerical examples with realistic traffic patterns from real video streams. It is shown that the proposed policies are able to keep the loss encountered by every user below its tolerable loss. The proposed policies are also able to achieve a significantly lower peak SNR degradation than the existing schemes.

Interaction matters: Co-viewing facilitates learning from live video streaming for elementary students.

With the development of information technology, co-viewing of live video streaming (LVS) has become a popular online learning method. However, existing studies have found inconsistent results regarding the effects of co-viewing, which could be due to the impact of learner-learner interactions. The present study tested the effects of co-viewing LVS on learning in elementary students, and whether learner-learner interaction moderated students' attention allocation, learning performance (i.e., retention and transfer), learning efficiency, and metacognition. The study used a one-way between-subjects design, with 86 participants assigned randomly to one of three groups: learning alone group, merely co-viewing group, or co-viewing with interaction group. Kruskal-Wallis H tests showed that students in the co-viewing with interaction group allocated more attention to their co-viewer and less to the LVS. However, ANOVA results indicated that they had the best learning performance and metacognition, and demonstrated the highest learning efficiency. Meanwhile, those co-viewing without interaction did not show significantly positive effects compared to those learning alone. The results of informal interviews were largely consistent with the above findings. The findings of the present study suggest the benefits of co-viewing with interaction, providing practical implications for the social context of learning from LVS for elementary students in particular.

Optimized Visual Internet of Things for Video Streaming Enhancement in 5G Sensor Network Devices.

The global expansion of the Visual Internet of Things (VIoT)'s deployment with multiple devices and sensor interconnections has been widespread. Frame collusion and buffering delays are the primary artifacts in the broad area of VIoT networking applications due to significant packet loss and network congestion. Numerous studies have been carried out on the impact of packet loss on Quality of Experience (QoE) for a wide range of applications. In this paper, a lossy video transmission framework for the VIoT considering the KNN classifier merged with the H.265 protocols. The performance of the proposed framework was assessed while considering the congestion of encrypted static images transmitted to the wireless sensor networks. The performance analysis of the proposed KNN-H.265 protocol is compared with the existing traditional H.265 and H.264 protocols. The analysis suggests that the traditional H.264 and H.265 protocols cause video conversation packet drops. The performance of the proposed protocol is estimated with the parameters of frame number, delay, throughput, packet loss ratio, and Peak Signal to Noise Ratio (PSNR) on MATLAB 2018a simulation software. The proposed model gives 4% and 6% better PSNR values than the existing two methods and better throughput.

Integrating Visual and Network Data with Deep Learning for Streaming Video Quality Assessment.

Existing video Quality-of-Experience (QoE) metrics rely on the decoded video for the estimation. In this work, we explore how the overall viewer experience, quantified via the QoE score, can be automatically derived using only information available before and during the transmission of videos, on the server side. To validate the merits of the proposed scheme, we consider a dataset of videos encoded and streamed under different conditions and train a novel deep learning architecture for estimating the QoE of the decoded video. The major novelty of our work is the exploitation and demonstration of cutting-edge deep learning techniques in automatically estimating video QoE scores. Our work significantly extends the existing approach for estimating the QoE in video streaming services by combining visual information and network conditions.

Enhancing 360 Video Streaming through Salient Content in Head-Mounted Displays.

Predicting where users will look inside head-mounted displays (HMDs) and fetching only the relevant content is an effective approach for streaming bulky 360 videos over bandwidth-constrained networks. Despite previous efforts, anticipating users' fast and sudden head movements is still difficult because there is a lack of clear understanding of the unique visual attention in 360 videos that dictates the users' head movement in HMDs. This in turn reduces the effectiveness of streaming systems and degrades the users' Quality of Experience. To address this issue, we propose to extract salient cues unique in the 360 video content to capture the attentive behavior of HMD users. Empowered by the newly discovered saliency features, we devise a head-movement prediction algorithm to accurately predict users' head orientations in the near future. A 360 video streaming framework that takes full advantage of the head movement predictor is proposed to enhance the quality of delivered 360 videos. Practical trace-driven results show that the proposed saliency-based 360 video streaming system reduces the stall duration by 65% and the stall count by 46%, while saving 31% more bandwidth than state-of-the-art approaches.

Reducing the Individual Carbon Impact of Video Streaming: A Seven-Week Intervention Using Information, Goal Setting, and Feedback.

This online intervention study examined whether system- and action-related information alone, together with goal setting, or together with goal setting and feedback helps people change their video streaming activities in a pro-environmental way. The participants (N = 92) documented their video streaming activities for one week prior to the intervention (week 1), three weeks after the onset of the intervention (weeks 2-4), and in a follow-up period two weeks later (week 7). A reduction of greenhouse gas emissions associated with video streaming was observed over the course of the intervention, together with reduced streaming durations and lowered resolution settings across all groups. There were no differences between the groups. It appears that as regards video streaming, information combined with self-monitoring has considerable potential to change individual behaviour and its associated ecological impact.

Video coding deep learning-based modeling for long life video streaming over next network generation.

Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.264/AVC codec, which is one of the most popular codecs used in video steaming and conferencing communications, is chosen as a case study in this paper. We model the predicted consumed power, the predicted perceived video quality, and the predicted required bandwidth for the video codec based on video resolution and quantization parameters. We train, validate, and test the developed models through extensive experiments using several video contents. Results show that an accurate model can be built for the needed purpose and the video streaming quality, required power consumption, and required bandwidth can be predicted accurately which can be utilized to enhance network availability in a cooperative environment.

Livestreaming Can Augment the Observation of Veterinary Post-mortem Examinations.

The creation of interactive livestreaming post-mortem examination sessions for veterinary students is described, including the technological and pedagogical issues that were considered and a detailed description of the solution developed. We used the Hero 7 Go Pro camera ( https://gopro.com/en/gb ) and livestreamed using Zoom ( https://explore.zoom.us/en/about/ ). We completed a thorough quantitative and qualitative analysis of the student perception of the value of the streaming platform and the sessions that were delivered to the second and third year students in the Bachelor of Veterinary Medicine and Surgery (BVMS) programme at the University of Glasgow. JISC Online surveys to BVMS2 and BVMS3 were central to the quantitative and qualitative analysis (MVLS Ethics reference 200,190,190).Students who responded to the survey found the material interesting, were able to interact effectively with the pathologists, enjoyed the "pathologists' eye" view that the system afforded, and enjoyed the ability to review and revise the video recording. The disadvantage some mentioned was not being in the appropriate professional space, i.e. the post-mortem facility, although a few students found this advantageous and suggested that this was a useful introduction to the post-mortem facility but without the cold/smell/noise to detract from their learning. In addition, a short explanation of additional uses of the Zoom Go Pro to teach BVMS4 and Veterinary Bioscience BSc Level 3 students and use for extracurricular student activities, e.g. Pathology Club, Student Chapter of the American Veterinary Medical Association at the University of Glasgow School of Veterinary Medicine, is given. The authors also consider other roles for the platform in the future, in particular the induction of students to the post-mortem facility environment.

A Novel Dynamic Bit Rate Analysis Technique for Adaptive Video Streaming over HTTP Support.

Recently, there has been an increase in research interest in the seamless streaming of video on top of Hypertext Transfer Protocol (HTTP) in cellular networks (3G/4G). The main challenges involved are the variation in available bit rates on the Internet caused by resource sharing and the dynamic nature of wireless communication channels. State-of-the-art techniques, such as Dynamic Adaptive Streaming over HTTP (DASH), support the streaming of stored video, but they suffer from the challenge of live video content due to fluctuating bit rate in the network. In this work, a novel dynamic bit rate analysis technique is proposed to model client-server architecture using attention-based long short-term memory (A-LSTM) networks for solving the problem of smooth video streaming over HTTP networks. The proposed client system analyzes the bit rate dynamically, and a status report is sent to the server to adjust the ongoing session parameter. The server assesses the dynamics of the bit rate on the fly and calculates the status for each video sequence. The bit rate and buffer length are given as sequential inputs to LSTM to produce feature vectors. These feature vectors are given different weights to produce updated feature vectors. These updated feature vectors are given to multi-layer feed forward neural networks to predict six output class labels (144p, 240p, 360p, 480p, 720p, and 1080p). Finally, the proposed A-LSTM work is evaluated in real-time using a code division multiple access evolution-data optimized network (CDMA20001xEVDO Rev-A) with the help of an Internet dongle. Furthermore, the performance is analyzed with the full reference quality metric of streaming video to validate our proposed work. Experimental results also show an average improvement of 37.53% in peak signal-to-noise ratio (PSNR) and 5.7% in structural similarity (SSIM) index over the commonly used buffer-filling technique during the live streaming of video.

HoloKinect: Holographic 3D Video Conferencing.

Recent world events have caused a dramatic rise in the use of video conferencing solutions such as Zoom and FaceTime. Although 3D capture and display technologies are becoming common in consumer products (e.g., Apple iPhone TrueDepth sensors, Microsoft Kinect devices, and Meta Quest VR headsets), 3D telecommunication has not yet seen any appreciable adoption. Researchers have made great progress in developing advanced 3D telepresence systems, but often with burdensome hardware and network requirements. In this work, we present HoloKinect, an open-source, user-friendly, and GPU-accelerated platform for enabling live, two-way 3D video conferencing on commodity hardware and a standard broadband internet connection. A Microsoft Azure Kinect serves as the capture device and a Looking Glass Portrait multiscopically displays the final reconstructed 3D mesh for a hologram-like effect. HoloKinect packs color and depth information into a single video stream, leveraging multiwavelength depth (MWD) encoding to store depth maps in standard RGB video frames. The video stream is compressed with highly optimized and hardware-accelerated video codecs such as H.264. A search of the depth and video encoding parameter space was performed to analyze the quantitative and qualitative losses resulting from HoloKinect's lossy compression scheme. Visual results were acceptable at all tested bitrates (3-30 Mbps), while the best results were achieved with higher video bitrates and full 4:4:4 chroma sampling. RMSE values of the recovered depth measurements were low across all settings permutations.

RTF-RCNN: An Architecture for Real-Time Tomato Plant Leaf Diseases Detection in Video Streaming Using Faster-RCNN.

In today's era, vegetables are considered a very important part of many foods. Even though every individual can harvest their vegetables in the home kitchen garden, in vegetable crops, Tomatoes are the most popular and can be used normally in every kind of food item. Tomato plants get affected by various diseases during their growing season, like many other crops. Normally, in tomato plants, 40-60% may be damaged due to leaf diseases in the field if the cultivators do not focus on control measures. In tomato production, these diseases can bring a great loss. Therefore, a proper mechanism is needed for the detection of these problems. Different techniques were proposed by researchers for detecting these plant diseases and these mechanisms are vector machines, artificial neural networks, and Convolutional Neural Network (CNN) models. In earlier times, a technique was used for detecting diseases called the benchmark feature extraction technique. In this area of study for detecting tomato plant diseases, another model was proposed, which was known as the real-time faster region convolutional neural network (RTF-RCNN) model, using both images and real-time video streaming. For the RTF-RCNN, we used different parameters like precision, accuracy, and recall while comparing them with the Alex net and CNN models. Hence the final result shows that the accuracy of the proposed RTF-RCNN is 97.42%, which is higher than the rate of the Alex net and CNN models, which were respectively 96.32% and 92.21%.

Factor structure, reliability and criterion-related validity of the English version of the Problematic Series Watching Scale.

BACKGROUND: Psychological research in the past decade has investigated the psychosocial implications of problematic use of on-demand online video streaming services, particularly series watching. Yet, a psychometric measure of problematic series watching in English is not available. AIMS: The present study aimed to test the factor structure, reliability and criterion-related validity of the English version of the Problematic Series Watching Scale, a six-item self-report assessing problematic series watching, based on the biopsychosocial components model of addiction. METHOD: Participants were recruited from two UK university student samples. Study 1 (n = 333) comprised confirmatory factor analysis, reliability tests and item response theory analyses to test the original unidimensional model and investigate each item's levels of discrimination and information. Study 2 (n = 209) comprised correlation analyses to test the criterion-related validity of the scale. RESULTS: There was a good fit of the theoretical model of the scale to the data (Comparative Fit Index = 0.998, Root Mean Square Error of Approximation = 0.024 [90% CI 0.000-0.093], Standardised Root Mean square Residual = 0.048), satisfactory reliability (ω = 0.79) and item levels of discrimination and information. The scale positively correlated with time spent watching series (rs = 0.26, P < 0.001) and negative affect (rs = 0.43, P < 0.001), and correlated negatively with positive affect (rs = -0.12, P > 0.05), mental well-being (rs = -0.25, P < 0.001) and sleep quality (rs = -0.14, P < 0.05). CONCLUSIONS: Results are discussed in relation to the ongoing debate on binge watching and series watching in the context of positive reinforcement versus problematic behaviour.

Machiavellianism and Gift-Giving in Live Video Streaming: The Mediating Role of Desire for Control and the Moderating Role of Materialism.

This study aimed to examine the association between Machiavellianism and gift-giving in live video streaming, as well as the mediating role of desire for control and the moderating role of materialism in this relation. A sample of 212 undergraduate students (146 males; the average age was 19.80 ± 2.05 years old) with experience of gift-giving in live video streaming was recruited to complete questionnaires on Machiavellianism, desire for control, materialism, and the frequency of gift-giving in live video streaming. The results showed that Machiavellianism was positively associated with gift-giving in live video streaming through the mediating role of desire for control; and the mediating effect of desire for control was moderated by materialism, with this relation being stronger for individuals with a higher level of materialism. Though with several limitations (e.g., cross-sectional method), this study could deepen our understanding of the influencing mechanism of gift-giving in live video streaming, which could also provide practical implications for the sustainable development of the live video streaming industry.

Machine Learning for Multimedia Communications.

Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

Diagnóstico intraoperatorio remoto en Patología con transmisión de vídeo de alta definición a través de YouTube y Twitch / Remote intraoperative frozen section diagnosis in pathology with high definition video transmission through YouTube and Twitch

Introducción: El diagnóstico intraoperatorio remoto aporta gran valor a hospitales que no disponen de patólogos a tiempo completo. Su desarrollo ha ido incorporando los continuos avances en tecnologías de la información. Hemos adaptado nuestros procedimientos con el uso de vídeo de alta definición transmitido en tiempo real. Material y métodos: Conectamos a nuestro microscopio una cámara de vídeo con capacidad de emisión a 1.080 píxeles de resolución y con una capturadora de vídeo enviamos la señal a un ordenador. El software OBS codifica y transmite las secuencias de vídeo a las plataformas de distribución YouTube y Twitch. Resultados: La calidad de imagen permite la valoración de cortes por congelación con garantías para la emisión del diagnóstico. El acceso a través de páginas web hace posible su visualización desde cualquier lugar y desde cualquier dispositivo con acceso a Internet. Discusión: El diagnóstico intraoperatorio remoto es un desafío para patólogos. La calidad de imagen es un requisito indispensable para su implantación, que la tecnología de streaming de vídeo resuelve. Como requisito adicional, es imprescindible la adecuada formación de los técnicos. Las aplicaciones del sistema pueden extenderse a otros muchos ámbitos de la Patología, especialmente en docencia y consultas.(AU)

Introduction: Remote diagnosis of frozen sections is an important asset for hospitals that do not have full-time pathologists. Ongoing advances in information technology are constantly being incorporated and we have used real time high-definition video. Material and methods: Our microscope was connected to a video camera with a 1080p resolution and its signal sent to the computer where OBS software encoded and transmitted video streams to YouTube and Twitch distribution platforms. Results: The high-quality image thus achieved allows an accurate, remote evaluation of frozen sections. Access through web pages allows them to be reviewed anywhere from any device with an Internet connection. Discussion: Remote intraoperative diagnosis is a challenge for pathologists and image quality is a critical requirement for its implementation, which can be solved by video streaming technology. The proper training of technicians is essential. This system can also be applied to many other areas of pathology, such as teaching and consultation.(AU)

[Remote intraoperative frozen section diagnosis in pathology with high definition video transmission through YouTube and Twitch]. / Diagnóstico intraoperatorio remoto en Patología con transmisión de vídeo de alta definición a través de YouTube y Twitch.

INTRODUCTION: Remote diagnosis of frozen sections is an important asset for hospitals that do not have full-time pathologists. Ongoing advances in information technology are constantly being incorporated and we have used real time high-definition video. MATERIAL AND METHODS: Our microscope was connected to a video camera with a 1080p resolution and its signal sent to the computer where OBS software encoded and transmitted video streams to YouTube and Twitch distribution platforms. RESULTS: The high-quality image thus achieved allows an accurate, remote evaluation of frozen sections. Access through web pages allows them to be reviewed anywhere from any device with an Internet connection. DISCUSSION: Remote intraoperative diagnosis is a challenge for pathologists and image quality is a critical requirement for its implementation, which can be solved by video streaming technology. The proper training of technicians is essential. This system can also be applied to many other areas of pathology, such as teaching and consultation.

Location-proximity-based clustering method for peer-to-peer multimedia streaming services with multiple sources.

Social distancing to reduce the spread of coronavirus disease 2019 (COVID-19) made a huge increase in the global OTT market, and OTT service providers get millions of new subscribers. Recently OTT service providers are extending their service to video broadcasting. As a one type of video broadcasting, this paper covers multimedia streaming with multiple sources. Multimedia streaming with multiple sources has multiple sources, and receivers can select one specific source to watch the video from the source. Sources include cameras capturing different angles of same event or location, cameras in geographical locations, etc. For delivering video to rapidly increasing number of users, multimedia streaming with multiple sources system needs efficient and scalable delivery method. Tree-based Peer-to-peer (P2P) networking has been investigated as the delivery solution of multimedia streaming with multiple sources, and set-top boxes or mobile apps of OTT service can be used as peers connecting the subscriber of OTT service. However, the scalability of the tree-based P2P networking is limited by the out-degree of a tree that branches linearly with the number of users. Hence, this study proposes clustering peers based on the location proximity of the peers to enhance the scalability of the P2P multimedia streaming with multiple sources. By clustering peers, one or more peers can be grouped into a virtual peer with an aggregated uplink/downlink capacity. This paper describes P2P multimedia streaming with multiple sources and algorithms for the proposed clustering method. Two applications which are one-view multiparty video conferencing and multi-view video streaming are introduced, and considerations for applying the proposed method to the applications are also discussed. The experimental results show that location-proximity-based clustering is effective in achieving a scalable P2P multimedia streaming with multiple sources by reducing the out-degree of a tree for the introduced applications. The proposed clustering leads improvement in the maximum achievable video bit rate, the average viewing video bit rate, and perceived delay.