ABSTRACT
The world have experienced a severe human-health crisis as a result of the emergence of a novel coronavirus (COVID-19), which was declared a global pandemic by WHO. As close human-to-human contact can spread the COVID-19 causing virus, keeping social distance is now an absolute necessity as a preventative measure. At a time of global pandemic, there is a huge need to treat patients with little patient–doctor interaction by using robots. Robots can be characterized as machines that can execute a wide range of tasks with greater autonomy and degree of freedom (DoF) than humans, making it difficult to identify them from other machines. A wide range of equipment, sensors, and information and communication technology (ICT) are now part of the healthcare system, which has become increasingly complicated. Protecting front-line personnel from virus exposure is the primary goal of using robots in health care. The aim of this study is to emphasize the evolving importance of robotics applications in health care and related fields. This paper examines in depth the design and operation of a wide range of healthcare robots in use around the world. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The world have experienced a severe human-health crisis as a result of the emergence of a novel coronavirus (COVID-19), which was declared a global pandemic by WHO. As close human-to-human contact can spread the COVID-19 causing virus, keeping social distance is now an absolute necessity as a preventative measure. At a time of global pandemic, there is a huge need to treat patients with little patient–doctor interaction by using robots. Robots can be characterized as machines that can execute a wide range of tasks with greater autonomy and degree of freedom (DoF) than humans, making it difficult to identify them from other machines. A wide range of equipment, sensors, and information and communication technology (ICT) are now part of the healthcare system, which has become increasingly complicated. Protecting front-line personnel from virus exposure is the primary goal of using robots in health care. The aim of this study is to emphasize the evolving importance of robotics applications in health care and related fields. This paper examines in depth the design and operation of a wide range of healthcare robots in use around the world. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
The ongoing COVID-19 pandemic has infected millions of people worldwide, overwhelming health infrastructures. The common symptoms are fever, cough, sore throat, muscle pain, headache, nausea, vomiting, and diarrhea, similar to the symptoms of common flu in mild and moderate cases. The distinguishing signs of common flu from that of COVID-19 are invisible until patients start feeling shortness of breath when the infection attacks the respiratory system in severe cases. At this stage, patients require immediate medical attention and hospitalization. In developing countries where health facilities are not adequate and costlier radiological tests like computed tomography (CT)-scans are scarce, diagnosis becomes a challenging task. The clarity comes only when a COVID- 19 test is conducted, which has its own time limitations depriving the patient of specialized treatment until the patient tests positive. In far to reach rural areas identification of COVID-19 induced pneumonia cases with the help of chest X-rays is more difficult with substandard medical infrastructure and a handful of expert radiologists available. This work detects COVID-19 induced Pneumonia with the help of chest X-rays. The used dataset includes COVID-19-infected patients' chest X-ray images as well as normal non-COVID chest X-Ray images. A Lightweight Stacked Convolutional Neural Network was created to extract fine details and information from images, assisting in the detection of COVID-19 caused pneumonia cases. For evaluation, we assessed the developed Neural Network model on a test validation set consisting of hundreds of chest X-Ray images. The suggested Neural Network's average test accuracy was determined to be 98.76%, with per-class accuracy of 99.40% for detecting COVID-19 cases and 98.42% for detecting normal cases.