Occupational Stress Monitoring Using Biomarkers and Smartwatches: A Systematic Review.

This article presents a systematic review of the literature concerning scientific publications on wrist wearables that can help to identify stress levels. The study is part of a research project aimed at modeling a stress surveillance system and providing coping recommendations. The investigation followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. In total, 38 articles were selected for full reading, and 10 articles were selected owing to their alignment with the study proposal. The types of technologies used in the research stand out amongst our main results after analyzing the articles. It is noteworthy that stress assessments are still based on standardized questionnaires, completed by the participants. The main biomarkers collected by the devices used in the selected works included: heart rate variation, cortisol analysis, skin conductance, body temperature, and blood volume at the wrist. This study concludes that developing a wrist wearable for stress identification using physiological and chemical sensors is challenging but possible and applicable.

Chest X-ray analysis empowered with deep learning: A systematic review.

Chest radiographs are widely used in the medical domain and at present, chest X-radiation particularly plays an important role in the diagnosis of medical conditions such as pneumonia and COVID-19 disease. The recent developments of deep learning techniques led to a promising performance in medical image classification and prediction tasks. With the availability of chest X-ray datasets and emerging trends in data engineering techniques, there is a growth in recent related publications. Recently, there have been only a few survey papers that addressed chest X-ray classification using deep learning techniques. However, they lack the analysis of the trends of recent studies. This systematic review paper explores and provides a comprehensive analysis of the related studies that have used deep learning techniques to analyze chest X-ray images. We present the state-of-the-art deep learning based pneumonia and COVID-19 detection solutions, trends in recent studies, publicly available datasets, guidance to follow a deep learning process, challenges and potential future research directions in this domain. The discoveries and the conclusions of the reviewed work have been organized in a way that researchers and developers working in the same domain can use this work to support them in taking decisions on their research.

Indoor Air Quality Monitoring Systems and COVID-19

Coronavirus pandemic is proven as wreaking havoc with the rising number of cases since its first identification in Wuhan, China. This invisible threat has taken approximately 1.06 M lives with more than 36.5 M cases worldwide as on October 9th, 2020. World Health Organization (WHO) estimated the mortality rate for COVID-19 to be around 3–4% with higher risk to the people that have underlying medical conditions such as respiratory disease, diabetes, cancer, heart disease, asthma, and kidney disease. Numerous public health experts have defined a clear link between coronavirus death rates and long-term exposure to air pollution, especially PM2.5 and NO2 levels. Medical health experts are working hard to find a reliable treatment for COVID-19. The need for real-time monitoring systems to promote indoor air quality is another critical concern that demands the attention of the research community. The main contribution of this study is to present the connection between COVID-19 pandemic, public health and indoor air quality while addressing the importance of real-time monitoring systems for public health and wellness. This chapter presents the necessity of developing indoor air quality monitoring systems for hospitals, schools, offices and homes for enhanced health and well-being.

A Survey of Using Machine Learning Algorithms During the COVID-19 Pandemic

The emergence of novel coronavirus (COVID-19) is considered a worldwide pandemic. In response to this pandemic and following the recent developments in artificial intelligence (AI) techniques, the literature witnessed an abundant amount of machine learning applications on COVID-19. To understand these applications, this study aims to provide an early review of the articles published on the employment of machine learning algorithms in predicting the COVID-19 infections, survival rates of patients, vaccine development, and drug discovery. While machine learning has had a more significant impact on healthcare, the analysis of the current review suggests that the use of machine learning is still in its early stages in fighting the COVID-19. Its practical application is hindered by the unavailability of large amounts of data. Other challenges, constraints, and future directions are also discussed.

Telemedicine and e-Health research solutions in literature for combatting COVID-19: a systematic review.

COVID-19 had led to severe clinical manifestations. In the current scenario, 98 794 942 people are infected, and it has responsible for 2 124 193 deaths around the world as reported by World Health Organization on 25 January 2021. Telemedicine has become a critical technology for providing medical care to patients by trying to reduce transmission of the virus among patients, families, and doctors. The economic consequences of coronavirus have affected the entire world and disrupted daily life in many countries. The development of telemedicine applications and eHealth services can significantly help to manage pandemic worldwide better. Consequently, the main objective of this paper is to present a systematic review of the implementation of telemedicine and e-health systems in the combat to COVID-19. The main contribution is to present a comprehensive description of the state of the art considering the domain areas, organizations, funding agencies, researcher units and authors involved. The results show that the United States and China have the most significant number of studies representing 42.11% and 31.58%, respectively. Furthermore, 35 different research units and 9 funding agencies are involved in the application of telemedicine systems to combat COVID-19.

Impact of COVID-19 on the psychological health of university students in Spain and their attitudes toward Mobile mental health solutions.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has had an impact on several aspects of life, including university students' mental health. Mobile mental care applications (apps) comprise a form of online mental care that enables the delivery of remote mental care. OBJECTIVES: This study aimed to explore the impact of COVID-19 on the mental health of university students in Spain and to explore their attitudes toward the use of mobile mental care apps. METHOD: Respondents answered a survey, which comprised two sections. The first included the 12-item General Health Questionnaire (GHQ-12) that was employed to assess the students' mental health. The second section included six questions developed by the authors to explore the students' attitudes toward mental care apps. RESULTS: The results showed that the students suffered from anxiety and depression as well as social dysfunction. Further, 91.3 % of the students had never used a mobile app for mental health, 36.3 % were unaware of such apps, and 79.2 % were willing to use them in the future. CONCLUSIONS: The COVID-19 pandemic had a significant impact on the psychological health of university students. Mobile mental care apps may be an effective and efficient way to access mental care, particularly during a pandemic.

A Systematic Literature Review on e-Mental Health Solutions to Assist Health Care Workers During COVID-19.

Background: e-Mental health is an established field of exploiting information and communication technologies for mental health care. It offers different solutions and has shown effectiveness in managing many psychological issues. Introduction: The coronavirus disease 2019 (COVID-19) pandemic has critically influenced health care systems and health care workers (HCWs). HCWs are working under hard conditions, and are suffering from different psychological issues, including anxiety, stress, and depression. Consequently, there is an undeniable need of mental care interventions for HCWs. Under the circumstances caused by COVID-19, e-health interventions can be used as tools to assist HCWs with their mental health. These solutions can provide mental health care support remotely, respecting the recommended safety measures. Materials and Methods: This study aims to identify e-mental health interventions, reported in the literature, that are developed for HCWs during the COVID-19 pandemic. A systematic literature review was conducted following the PRISMA protocol by searching the following digital libraries: IEEE, ACM, ScienceDirect, Scopus, and PubMed. Results and Discussion: Eleven publications were selected. The identified e-mental health interventions consisted of social media platforms, e-learning content, online resources and mobile applications. Only 27% of the studies included empirical evaluation of the reported interventions, 55% listed challenges and limitations related to the adoption of the reported interventions. And 45% presented interventions developed specifically for HCWs in China. The overall feedback on the identified interventions was positive, yet a lack of empirical evaluation was identified, especially regarding qualitative evidence. Conclusions: The COVID-19 pandemic has highlighted the importance and need for e-mental health solutions for HCWs.

Automated medical diagnosis of COVID-19 through EfficientNet convolutional neural network.

COVID-19 infection was reported in December 2019 at Wuhan, China. This virus critically affects several countries such as the USA, Brazil, India and Italy. Numerous research units are working at their higher level of effort to develop novel methods to prevent and control this pandemic scenario. The main objective of this paper is to propose a medical decision support system using the implementation of a convolutional neural network (CNN). This CNN has been developed using EfficientNet architecture. To the best of the authors' knowledge, there is no similar study that proposes an automated method for COVID-19 diagnosis using EfficientNet. Therefore, the main contribution is to present the results of a CNN developed using EfficientNet and 10-fold stratified cross-validation. This paper presents two main experiments. First, the binary classification results using images from COVID-19 patients and normal patients are shown. Second, the multi-class results using images from COVID-19, pneumonia and normal patients are discussed. The results show average accuracy values for binary and multi-class of 99.62% and 96.70%, respectively. On the one hand, the proposed CNN model using EfficientNet presents an average recall value of 99.63% and 96.69% concerning binary and multi-class, respectively. On the other hand, 99.64% is the average precision value reported by binary classification, and 97.54% is presented in multi-class. Finally, the average F1-score for multi-class is 97.11%, and 99.62% is presented for binary classification. In conclusion, the proposed architecture can provide an automated medical diagnostics system to support healthcare specialists for enhanced decision making during this pandemic scenario.