Role of telemedicine in COVID-19 pandemic – An overview

COVID-19 pandemic has caused worldwide havoc. The present healthcare system is ruined, distancing both patients and doctors. To avoid transmission of infection, travel restrictions were made, making the patient’s visit to the doctor difficult. However, routine checkups for patients not infected with COVID-19 should be given without the threat of getting exposed to other patients in the hospital. In this respect, telemedicine becomes a reliable source that provides health services as well as reduces the infection spread. The first known evidence of real-time (live) video consultation occurred in 1959 at the University of Nebraska where doctors used telemedicine to transmit neurological examinations to students. During the first 3 months of 2020, there was a 50% increase in telehealth visits. Most patients from January to March 2020 approached for a condition other than COVID-19. Many telemedicine applications have been launched for teleconsultation such as Practo, Mfine, TATA Health, and Doctor 24 × 7 provide teleconsultation. Telemedicine has shown a huge impact on the present healthcare system during this pandemic and has placed itself in the spotlight during recent times. Through this paper, the role of telemedicine in the current scenario of pandemics and its development through recent times is discussed.

A STUDY ON CHANNEL AND DELAY-BASED SCHEDULING ALGORITHMS FOR LIVE VIDEO STREAMING IN THE FIFTH GENERATION LONG TERM EVOLUTION-ADVANCED

This paper investigates the performance of a number of channel and delay-based scheduling algorithms for an efficient QoS (Quality of Service) provision with more live video streaming users over the Fifth Generation Long-Term Evolution-Advanced (5G LTE-A) network. These algorithms were developed for use in legacy wireless networks and minor changes were made to enable these algorithms to perform packet scheduling in the downlink 5G LTE-A. The efficacies of the EXP and M-LWDF algorithms in maximizing the number of live video streaming users at the desired transmission reliability, minimizing the average network delay and maximizing network throughput, are shown via simulations. As the M-LWDF has a simpler mathematical equation as compared to the EXP, it is more favoured for implementation in the complex downlink 5G LTE-A. ABSTRAK: Kertas ini menyiasat prestasi sebilangan saluran dan algoritma penjadualan berdasarkan kelewatan untuk penyediaan QoS (Kualiti Perkhidmatan) yang cekap dengan banyak pengguna video secara langsung melalui rangkaian Generasi Kelima Long-Term Evolution Advanced (5G LTE-A). Algoritma-algoritma yang disiasat di dalam kertas ini dicadangkan untuk digunakan dalam generasi rangkaian tanpa wayar yang lama dan sedikit perubahan dibuat untuk membolehkan algoritma ini menyokong penjadualan paket dalam downlink 5G LTE-A. Keberkesanan EXP dan M-LWDF algoritma dalam memaksimumkan jumlah pengguna pada kebolehpercayaan transmisi yang diinginkan dari streaming video secara langsung, meminimumkan kelewatan rangkaian, dan memaksimumkan truput rangkaian ditunjukkan melalui simulasi. Namun, dengan M-LWDF mempunyai formula matematik yang mudah dibandingkan dengan EXP, ia lebih sesuai untuk digunakan dalam downlink 5G LTE-A yang lebih kompleks.

Caracterización de tráfico para el servicio de Video Streaming en vivo sobre DASH en redes 4G basado en analizadores sintácticos

Contexto: El tráfico de datos móviles generado por los servicios de video aumenta a diario. Para enfrentar dicha situación, los proveedores de servicios de telecomunicaciones deben conocer el comportamiento del tráfico de video y así ajustar los recursos de la red que permitan satisfacer y mantener los niveles de calidad requeridos por los usuarios. Los estudios de caracterización de tráfico en redes 4G para el servicio Live Video Streaming (LVS) son escasos y los disponibles son obtenidos a partir de escenarios de simulación en los cuales no se consideran las condiciones reales de funcionamiento de este tipo de redes. Método: Este trabajo se centra en encontrar un modelado que caracterice el tráfico a partir de las funciones de densidad de probabilidad del servicio LVS bajo la técnica de streaming adaptativo DASH en redes LTE. Las trazas analizadas para realizar el estudio del modelado fueron adquiridas en escenarios reales de emulación considerando las condiciones de funcionamiento frecuentemente presentadas en la prestación real del servicio, para lo cual se definieron cinco escenarios de prueba. Resultados: Se presenta la descripción, a partir de la parametrización de algunas funciones de densidad de probabilidad encontradas, de diferentes modelos de tráfico del servicio bajo estudio y para cada uno de los escenarios de prueba preestablecidos en una red 4G-LTE. Conclusiones: A partir de los resultados, se concluye que el modelo de tráfico depende de las condiciones de cada escenario y que no existe un modelo único que describa el comportamiento general del servicio LVS bajo la técnica de streaming adaptativo DASH en una la red LTE emulada.

Students Attendances System Using Face Recognition

Now a days we are living in this world where everything is automated and linked online. Internet are the things discover and it is used all over a world very beneficially.in human body face is the crucial factor for identifying each person. It can be identified by using different method like biometric for taking attendance. But in this method many more time are required to take attendance and also people are in contact with each other while marking their attendance in this pandemic situation we are introducing new technology student attendance system using face reorganization. Generally in a classroom the attendance was taken manually at ending or beginning of the class. The problem is that they required a lot of time to be taken and some manual and paper work will make a chance of mistake. To overcome from this problem we are introducing face recognition base attendance system. It is used in many application for identification of human face in a digital image or live video stream video. The proposed system make used of Haar classifier, KNN, CNN, SVM and global filters. After this recognition attendance report will be generated in excel format. The overall accuracy and complexity are calculated after testing this system it is cost efficient and need less installation time. Keywords: Face recognitions, Face detection, Haar classifier, CNN, KNN, SVM, LBPH, Automatic Attendances and image processing.

A Novel Model for Large-Scale Online College Learning in Postpandemic Era: AI-Driven Approach

COVID-19 is a pandemic with a wide reach and explosive magnitude, and the world has been bracing itself for impact. Many have lost their jobs and savings, and many are homeless. For better or worse, COVID-19 has permanently changed our lives. For college students, the pandemic means giving up most of the on-campus experience in the postpandemic era and performing online learning instead. Virtual lessons may become a permanent part of college education. Large-scale online learning typically utilizes interactive live video streaming. In this study, we analyzed a codec and video streaming transmission protocol using artificial intelligence. First, we studied an intraframe prediction optimization algorithm for the H.266 codec based on long short-term memory networks. In terms of video streaming transmission protocols, real-time communication optimization based on Quick UDP Internet connections and Luby Transform codes is proposed to improve the quality of interactive live video streaming. Experimental results demonstrate that the proposed strategy outperforms three benchmarks in terms of video streaming quality, video streaming latency, and average throughput.

Reducing Loneliness with an Interactive Live Video Intervention: Experiment (Preprint)

BACKGROUND Loneliness, especially when chronic, can substantially reduce one’s quality of life. Positive social experiences might help to break cycles of loneliness by promoting more prosocial cognitions and behaviors. Internet-mediated live video communication platforms (eg, Zoom and Twitch) may offer an engaging and accessible medium to deliver such positive social experiences to people at scale. Despite their widespread use, there is a lack of research into how these newer platforms’ socially interactive elements affect loneliness-related aspects of users’ psychosocial well-being. OBJECTIVE We aimed to experimentally evaluate whether a socially interactive live video experience improved loneliness-related outcomes to a greater extent than a non-interactive control experience. METHODS We recruited participants from an online survey recruitment platform and assigned half to participate in a socially interactive live video experience with strangers and the other half to a non-interactive control experience that was designed to be identical in all other regards. Participants completed several baseline measures of psychosocial wellbeing, participated in an hour-long live video experience, and then completed some of the baseline measures again. Four weeks later, we followed up with participants to evaluate their change in trait loneliness since baseline. We pre-registered our hypotheses and analysis plan and provide our data, analysis code, and study materials online. RESULTS 249 participants completed the initial study and met inclusion criteria, 199 of whom also completed the 4-week follow-up. Consistent with our predictions, we found that directly after the more socially interactive experience, participants’ feelings of connectedness increased more (p<.001), positive affect increased more (p=.002), feelings of loneliness decreased more (p<.001), social threat decreased more (p=.006), and negative affect decreased more (p=.003) than they did after the less interactive experience. However, the extent of change in trait loneliness between baseline and 4 weeks later did not differ between conditions (p=.853). Future research is needed to examine how these effects might generalize across different contexts and populations, particularly in instances where participants have an expectation of future interaction. CONCLUSIONS Including socially interactive components in live video experiences can improve loneliness-related psychosocial outcomes for a short time. Future work should explore how these benefits can be leveraged towards longer-term prosociality.

The use of a long-acting tranquilizer (zuclopenthixol acetate) and live video monitoring for successful long-distance transport of caribou (Rangifer tarandus)

Long-distance transport of caribou (Rangifer tarandus) can result in morbidities and mortalities. This case report describes the use of a long-acting tranquilizer, zuclopenthixol acetate (ZA) and live video monitoring (LVM) to transport caribou over 2300 kilometers. Two groups of captive caribou were transported from Fort St. John, British Columbia to Dryden, Ontario (n=14; 28.5 h transport) and Anchorage, Alaska (n=11; 32 h transport). The day prior to transport, caribou were administered ZA at 1 mg/kg via deep intramuscular (IM) injection. Digital video cameras allowed for live observation of caribou during transport. Still images of videos from each compartment in the Ontario transport were analyzed for percentage (%) lying versus standing over three time periods (Day 1, Night, and Day 2). Overall, caribou spent 57% of the transport lying down, with the highest percentage occurring at night (73%). As group size and animal density decreased there was a trend for caribou to spend more time lying down. Three animals developed extrapyramidal effects to the ZA and were effectively treated with midazolam at 0.2 mg/kg IM. There were no significant visible injuries or mortalities during or up to 6 weeks post-transport. Zuclopenthixol acetate and LVM were used to successfully transport caribou over long distances and should be considered in future translocations to improve animal welfare during transport.

Data-Driven Facial Expression Analysis from Live Video

<p>Emotion analytics is the study of human behavior by analyzing the responses when humans experience different emotions. In this thesis, we research into emotion analytics solutions using computer vision to detect emotions from facial expressions automatically using live video. Considering anxiety is an emotion that can lead to more serious conditions like anxiety disorders and depression, we propose 2 hypotheses to detect anxiety from facial expressions. One hypothesis is that the complex emotion “anxiety” is a subset of the basic emotion “fear”. The other hypothesis is that anxiety can be distinguished from fear by differences in head and eye motion. We test the first hypothesis by implementing a basic emotions detector based on facial action coding system (FACS) to detect fear from videos of anxious faces. When we discover that this is not as accurate as we would like, an alternative solution based on Gabor filters is implemented. A comparison is done between the solutions and the Gabor-based solution is found to be inferior. The second hypothesis is tested by using scatter graphs and statistical analysis of the head and eye motions of videos for fear and anxiety expressions. It is found that head pitch has significant differences between fear and anxiety. As a conclusion to the thesis, we implement a systems software using the basic emotions detector based on FACS and evaluate the software by comparing commercials using emotions detected from facial expressions of viewers.</p>