ABSTRACT
The Internet of Things revolution is transforming current healthcare practices by combining technological, economic, and social aspects. Since December 2019, the global spread of COVID19 has influenced the global economy. The COVID19 epidemic has forced governments all around the world to implement lockdowns to prevent viral infections. Wearing a face mask in a public location, according to survey results, greatly minimizes the risk of infection. The suggested robotics design includes an IoT solution for facemask detection, body temperature detection, an automatic dispenser for hand sanitizing, and a social distance monitoring system that can be used in any public space as a single IoT solution. Our goal was to use IoT-enabled technology to help prevent the spread of COVID19, with encouraging results and a future Smart Robot that Aids in COVID19 Prevention. Arduino NANO, MCU unit, ultrasonic sensor, IR sensor, temperature sensor, and buzzer are all part of our suggested implementation system. Our system's processing components, the Arduino UNO and MCU modules are all employed to process and output data. Countries with large populations, such as India and Bangladesh, as well as any other developing country, will benefit from using our cost-effective, trustworthy, and portable smart robots to effectively reduce COVID-19 viral transmission. © 2022 IEEE.
ABSTRACT
The coronavirus outbreak has significantly affected the health and well-being of several people around the world. In a similar vein, Bangladeshi medical professionals have also been affected by several severe physical and mental health complications resulting from their frequent contact with COVID-19 patients. This exposes them to a greater risk of infection with the lethal virus, which can substantially impact their job performance. Therefore, this research aims to investigate the manner in which the COVID-19 pandemic affects the occupational health and safety of medical employees. The researchers deployed a descriptive qualitative technique to investigate the complexities of the COVID-19 crisis amongst medical practitioners. Employing purposeful sampling and in-depth interview techniques, the researchers collected data from a total of 32 healthcare professionals and investigated their state of occupational health, their exposure to stress and trauma, and the effects of stress and trauma on their livelihood, health and well-being. The data revealed the occupational health of healthcare workers as being fragile, resulting to stress and trauma, and eventually, a depressed state of mind. To address this issue, relevant government and non-governmental organizations should concentrate on reducing COVID-19-related risks and repercussions in hospital settings. In addition, policymakers, social workers, public health practitioners and psychologists must work together to ensure that healthcare workers are healthy and safe at work. © 2023, Editura Lumen. All rights reserved.
ABSTRACT
Importance of online education can be seen especially during the ongoing Covid-19 when going to schools or colleges is not possible. So validity of online exams should also be maintained with respect to traditional pen-paper examinations. However, absence of invigilator makes it easy for the examinees to cheat during the exam. Though there are already many systems for online proctoring, not all educational institutes can afford them as the systems are very expensive. In this paper, we have used eye gaze and head pose estimation as the main features to design our online proctoring system. Therefore, the purpose of this paper is to use these features to create an online proctoring system using computer vision and machine learning and stop cheating attempts in exams. © 2021 IEEE.
ABSTRACT
Introduction: The purpose of this research was to predict mental illness among university students using various machine learning (ML) algorithms. Methods: A structured questionnaire-based online survey was conducted on 2121 university students (private and public) living in Bangladesh. After obtaining informed consent, the participants completed a web-based survey examining sociodemographic variables and behavioral tests (including the Patient Health Questionnaire (PHQ-9) scale and the Generalized Anxiety Disorder Assessment-7 scale). This study applied six well-known ML algorithms, namely logistic regression, random forest (RF), support vector machine (SVM), linear discriminate analysis, K-nearest neighbors, Naive Bayes, and which were used to predict mental illness among university students from Dhaka city in Bangladesh. Results: Of the 2121 eligible respondents, 45% were male and 55% were female, and approximately 76.9% were 21-25 years old. The prevalence of severe depression and severe anxiety was higher for women than for men. Based on various performance parameters, the results of the accuracy assessment showed that RF outperformed other models for the prediction of depression (89% accuracy), while SVM provided the best result than other models for the prediction of anxiety (91.49% accuracy). Conclusion: Based on these findings, we recommend that the RF algorithm and the SVM algorithm were more moderate than any other ML algorithm used in this study to predict the mental health status of university students in Bangladesh (depression and anxiety, respectively). Finally, this study proposes to apply RF and SVM classification when the prediction of mental illness status is the core interest.
ABSTRACT
Introduction: Handwashing practice is an effective way to minimize severe infectious diseases such as COVID-19, diarrhea, and pneumonia. The study aimed to explore the prevalence of handwashing behavior and associated determinants in rural and urban areas of Bangladesh. Methods: The research was performed using cross-sectional survey data from the 2019 Bangladesh Multiple Indicator Cluster Survey, and 61,242 household members were the sample for this study. The Chi-square test was applied for the bivariate analysis. A generalized linear mixed-effects model was used to identify the risk factors of practicing handwashing in both urban and rural areas of Bangladesh. Results: Only 65% of the country's households had access to handwashing facilities (place, water, and materials). While urban dwellers were more likely to wash their hands, rural dwellers were only 63% likely to do so. The level of education of household heads, wealth status, division, number of family members, sanitation facilities, and water source were the key factors associated with handwashing behavior. This study revealed that the odds were significantly lower among illiterate respondents compared to those with secondary and above-secondary education in both areas of Bangladesh, and a positive association was found between wealth status and handwashing behavior practiced in both urban and rural areas. In this study, the size of the family was statistically significant for both rural and urban areas of Bangladesh. Conclusion: Handwashing is the most prominent consideration for controlling COVID-19. Policymakers are striving to improve handwashing facilities by increasing awareness-raising programs, especially among rural residents of Bangladesh. © 2022 Asian Journal of Social Health and Behavior ;Published by Wolters Kluwer - Medknow.
ABSTRACT
Introduction: Lung cancer is the leading cause of cancer death worldwide and Covid-19 pandemic has exacerbated the problem much more. There is a high risk of being infected with SARS-CoV-2 among patients having lung cancer. This study aims to assess the knowledge, attitudes, and help-seeking for early symptoms of lung cancer in Bangladeshi people. Method: A cross-sectional study was conducted with 744 randomly selected respondents from eight different administrative regions of Bangladesh between June and August 2021. A structured questionnaire was used covering socio-demographic characteristics of the participants including their knowledge, attitudes, and participant's risk about lung cancer to accomplish our aim and objectives. Multivariable logistic regression models were used to identify factors associated with the knowledge and awareness of lung cancer. Result: Of the 744 participants, 90.3% (672/744) reported to have heard about lung cancer. A total of 17 participants were identified as lung cancer patients. Being a smoker (96.7%) and unexplained shortness of breath (92.6%) were identified as the most common risk factor and symptoms of lung cancer respectively. Among the socio-demographic variables, the level of education of the respondents was identified as an independent predictor for both knowledge (p<0.001) and awareness (p<0.001) about lung cancer. Smoking status was significantly associated with the participant's awareness of lung cancer (p<0.001). Conclusion: Although most participants were knowledgeable about smoking as a major risk factor, it was not proportional to their actions to stop smoking. This study highlights the importance of raising awareness and enhancing positive steps to avoid modified risk factors or even encourage early testing for lung cancer.
ABSTRACT
The study was aimed to assess the psychological aspects and relevant factors of the health-care workers (HCWs) working in COVID 19 pandemic condition in Bangladesh. This online cross-sectional survey was conducted from different tertiary, secondary and primary hospitals in Bangladesh. Eligible 638 HCWs who were directly involved in the caring of confirmed or suspected COVID-19 patients were recruited in this study. The mental health was assessed by the Patient Health Questionnare-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7) and Athens Insomnia Scale (AIS). High frequency of depression 536(84.0%), anxiety 386(60.5%) and insomnia 302(47.3%) was found among the HCWs, which were significantly higher in physicians (p<0.001) than nurses. Moderate to severe depression was significantly higher in female, whereas minimal to mild depression was significant in male HCWs (p=0.014). Symptoms of depression (p<0.001), anxiety (p<0.001) and insomnia (p=0.004) were significantly higher among the HCWs of primary and secondary compared to the tertiary level. The HCWs developed psychological trauma due to family health (45.3%) and contagious disease property (66.6%). After adjusting confounders, multivariable logistic regression analysis showed that physicians and HCWs of secondary hospital had significant symptoms of severe depression (OR=2.95, 95% CI=0.50-17.24;p<0.001), anxiety (OR=2.64, 95% CI=0.80-8.72;p<0.001) and insomnia (OR=2.67, 95% CI=1.23-5.84;p=0.018);whereas female HCWs had more risk of developing symptoms of severe insomnia (OR= 1.84;95% CI=1.23-2.75;p=0.003). High rate of depression, anxiety and insomnia was found among HCWs working in the COVID-19 pandemic condition in this survey.
ABSTRACT
As the deadly COVID-19 outbreak spreads across the globe, the utilization of IoT in the surveillance of patients can prevent us from facing catastrophic repercussions. This paper aims to develop a real-time health monitoring system where sensors are used to continuously observe the patient's body temperature, heart rate, and oxygen level. A comparison of two CNN architectures, VGG19 and DenseNet was also undertaken for audio signal processing, with VGG19 offering more promising accuracy in identifying coughing. Additionally, as severe coughing can be an alarm for lung diseases, the system identifies the number of consecutive coughing of a patient, as well as the timestamp for it. Moreover, if a patient feels infirm, they can seek assistance from a nearby doctor or nurse through Google's Speech-to-Text API. The data is then transmitted to a centralized database, where clinicians can monitor patients' symptoms in real-time by extracting the data via a web application. © 2021 IEEE.
ABSTRACT
Introduction: The coronavirus disease 2019 (COVID-19) has become a public health concern, and behavioral adjustments will minimize its spread worldwide by 80%. The main purpose of this research was to examine the factors associated with concerns about COVID-19 and the future direction of the COVID-19 scenario of Bangladesh.
ABSTRACT
In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.