Human Activity Recognition Using Machine Learning Technique

COVID has made education shift towards online mode. In online mode, instructors have a hard time keeping track of their students. Students' performance in online classes falls considerably below the level of learning due to a lack of attention. This initiative aids in the supervision of students during online classes. Artificial Intelligence (AI) models are being developed to better recognize student activities during online sessions. Many applications rely on determining an individual's mental state. When evaluating which subtask is the most challenging, a quantitative measure of human activity while executing a task can be helpful. Thus, the goal of this research is to create an algorithm that uses EEG data gathered with a Muse headset to measure the amount of cognitive intelligence of students during online classes. The data collected by the Muse headset is multidimensional, and it is pre-processed before being fed into machine learning algorithms. Using feature selection, the dataset's dimension is now reduced. The model's precision and recall were calculated, and a confusion matrix was created. The Support Vector Machine produces better outcomes in the experiment. © 2022 IEEE.

A Survey On Different Methods For Remote Health Monitoring In Covid 19 Diagnosis

The outburst of the coronavirus is in the phase of development due to lack of precise diagnosis needed by patients. Because the virus spreads quickly from a corona positive individual to the others, the health of any area that is densely inhabited is quite important. Presently, medical community has reached a disaster level since the number of cases are rising very fast and the medical facilities are very limited. Therefore, it is high time that an intelligent model is conceived and developed for monitoring the symptoms of patient health online, then estimate and find the anomaly in the health status of patient further. By assessing the tracked health parameters in this way, a corona virus-infected patient's health condition may be analysed using a well-adapted prediction model. In this study, an overview on the benchmark approaches utilized in recent remote monitoring in covid 19 diagnosis studies is provided where the different algorithms are elucidated and the comparison between the advantages is performed. Also, the problems and drawbacks that covid 19 diagnosis research encounters commonly is discussed. On the whole, this article comprehensively delves into the field of automated remote monitoring in covid 19 diagnosis and also the future research endeavors in this field are put forward. Copyright © 2022 Authors. All rights reserved.

Human Activity Recognition Using LSTM Architecture

The covid 19 pandemic has made everything virtual, including education. It is difficult to tell if students are focused or not due to online education. To help teachers, we are developing a framework for recognizing and assessing student focus. By using the concept of brain-computer communication, we can find the student's concentration level. The data obtained from the electroencephalogram (EEG) signals is used as a data set to predict concentration levels. A four-channel device is used to capture brain waves. The data were preprocessed and feature extraction was performed to determine the concentration level as active or inactive. In this method, we use a multiclass approach to develop a deep learning model that uses LSTM to classify concentration into low, or high concentration levels with accuracy of 88%. © 2022 IEEE.

The Association of Sars CoV-2 Cycle threshold (CT VALUE) with Severity and Outcomes in SARS CoV-2

Background & Objectives: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19 is a novel betacoronavirus that first appeared in Wuhan, Hubei Province, China in late December 2019. Real-time reverse-transcription polymerase chain reaction (RT-PCR) test has been routinely used as the diagnostic test to detect SARS-CoV-2 nucleic acids in respiratory specimen swabs from throat, nasopharynx, and nose of individuals suspected of COVID-19 during the acute phase of infection. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) cycle threshold (Ct) has been suggested as an approximate measure of initial viral burden. So this study was undertaken retrospectively to evaluate association of CT values of patients tested positive for SARS CoV 2 by RT-PCR with clinical features and the outcomes of patients. Material(s) and Method(s): A retrospective study was performed among patients tested positive for SARS CoV 2 by RT-PCR & admitted in at Dr.Chandramma Dayananda Sagar institute of Medical education and research, Harohalli, Ramanagara. during the period of 08 months from January 2021 to August 2021. The details of the patients on duration of hospital stay, age, presence of comorbidities, intubation, mortality were recorded. The obtained CT values were classified into 3 Groups: Group 1 > 30.0, Group 2-25.0-29.9, Group 3 < 25. The data was analysed using descriptive statistics. Result(s): The study comprised of 668 (men 451,women 217) patients tested positive for SARS CoV 2 by RT-PCR. The mean age in men was 51 years and women was 38 years. 209 patients of Group 3, (31.2%) patients were significantly higher age, co-morbidities like Hypertension, Diabetes mellitus and obstructive lung disease, more than 418 patients of Group 2 (62.5%) and 41 patients of Group 1 (6.13%) patients. Duration of hospital stay was 14 days in Group 3 compared to Group 2 and Group 1 patients. Noninvasive ventilation (12.8%), Invasive ventilation (11.4%) and mortality (11.6%) is more in Group 3 compared to Group 2 and Group 1 patients. Conclusion(s): This study provide evidence that Low CT values suggestive of greater SARS CoV 2 viral load are associated with the severity of illness and duration of hospitalization. CT values may allow for early risk stratification in patients with SARS CoV 2. Copyright © 2022 Ubiquity Press. All rights reserved.

Exosomes/EVs: CASE SERIES: THE COMBINATION OF RECOMMENDED TREATMENT PROTOCOLS WITH ADD-ON SECRETOME FROM UMBILICAL CORD-DERIVED MESENCHYMAL STEM CELLS (UC-MSCS) THERAPY AS A MEDICAL STRATEGY FOR COVID-19 TREATMENT

Background & Aim: COVID-19 suggested to be treated with pleiotropic agents rather than single-target agents due to its complicated pathophysiology. There are currently no approved effective therapy that can stop the progression of COVID-19. Mesenchymal stem cells and its secretome have been studied in several in vivo lung disease models. The therapeutic application of Secretome therapy has been shown to be efficient in influenza infection, resulting in decreased alveolar fluid clearance and lung injury. This has been linked to attenuation of pro-inflammatory cytokine release, inflammatory cell recruitment, and increased alveolar macrophage content. In this article, we report case series of three COVID-19 patients received an experimental treatment with secretome from umbilical cord-derived mesenchymal stem cells (UC-MSCs) therapy in conjunction with recommended treatment protocols. Methods, Results & Conclusion: Three male patients who were tested positive for COVID-19 are initially presented with mild case of COVID-19 symptoms. The patients were treated with recommended treatment protocols of COVID-19 and add-on secretome from umbilical cord-derived mesenchymal stem cells (UC-MSCs) therapy. In this cases we report the administration of 1 cc and 0,5 cc secretome from UC-MSCs through nasal drop, with 0,5 cc and 0,25 cc for each nostril. Ground Glass Opacity (GGO) were checked by chest CT and the observation stop when patients were symptom-free and tested negative for (Figure Presented)Figs 1 and 2 COVID-19. Significant improvement showed in patients’s respiratory symptoms include GGO profiles, proven by chest CT and no side effects reported. Our report showed that nasal drop of secretome from UC-MSC therapy in patients with mild COVID-19 is safe and well tolerated. No serious therapy-associated adverse event was observed. Further study with more patients and comprehensive biomarker testing is needed to evaluate the efficacy of secretome from UC-MSC therapy to improve long-term treatment outcomes in COVID-19 patients

Monitoring and Controlling of ICU Environmental Status with WiFi Network Implementation on Zynq SoC

The role of embedded systems in medical applications is very crucial. The development of such applications made by ASICs is much expensive and includes high NRE cost and no enhancements are possible because they are one-time programmable. Therefore, a better solution is available with reconfigurable PLDs. In the present pandemic Covid 19 situation, there is a requirement for a lot of medical facilities at a lower cost with accurate monitoring and controlling is required. Therefore, the proposed medical application is an ICU monitoring and control system with a WiFi network, implemented on a Zynq architecture-based FPGA development board. The functionality of this system is, the ICU environmental parameters are monitored and displayed on OLED display and control of various appliances remotely within the vicinity of WiFi network. Such systems are useful in both Government and corporate hospitals to have the flexibility and smooth monitoring of ICUs. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Role of LIS Professionals to provide authentic information sources during COVID-19 a Pandemic crisis

During the national crisis, caused by a novel pandemic such as COVID-19 virus infections, people need authentic information to face such a virus. Libraries need to come forward in such a situation as primary sources of trustworthy information by providing rapid and easy access to those seeking authentic information sources. In this crisis, giving Remote Access, Virtual Library, e-resources, Ask the Librarian reference services to the user in real-time and at scale for a range of scientific- and policy-related responses can be very significant to the academic and research community. The present study has collected data from authentic information sources, mainly Government official portals of Indian states such as the MyGov COVID-19, UTs, and National/International agencies, etc. After analyzing and studying the data, the authentic information sources served to the research community are listed out. This paper also highlights the ratio of COVID-19 publications and highly cited publication of the same, using the Scopus abstracting databases. This study is an original research work focused on the roles and responsibilities and actions taken by the Library and librarians in crisis situations. Hence this study will help Librarians, Information Providers, Practitioners, Policy Makers, Researchers, and development activity in this field. © 2020. All Rights Reserved.