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 new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system. However, this contagious illness can be controlled by appropriately donning a facial mask. If people keep a strong social distance and wear face masks, COVID-19 can be controlled. A method for detecting these violations is proposed in this paper. These infractions include failing to wear a facemask and failing to maintain social distancing. To train a deep learning architecture, a dataset compiled from several sources is used. To compute the distance between two people in a particular area and also predicts the people wearing and not wearing the mask, The proposed system makes use of YOLOv3 architecture and computer vision. The goal of this research is to provide valuable tool for reducing the transmission of this contagious disease in various environments, including streets and supermarkets. The proposed system is evaluated using the COCO dataset. It is evident from the experimental analysis that the proposed system performs well in predicting the people wearing the mask because it has acquired an accuracy of 99.2% and an F1-score of 0.99.
ABSTRACT
As per researchers worldwide, the practice of social distancing and wearing masks may have to be continued till the end of 2022 or even after considering the deadly and unpredictable nature of the COVID-19 virus that has gripped our world. In spite of all the rules and regulations in place we still find people breaking the rules. Also, it is to be noted that many officials who are tasked with enforcing the rules have either been severely affected or have lost their lives. Our work focuses on creating a holonomic robot, that can detect violations to mask and social distancing rules and generate alerts. Our work uses OpenCV and CNN model for face mask detection and pre-trained Mobile-Net Single Shot Object Detection (SSD) model to detect people and check if social distancing is adhered to. © 2021 IEEE.
ABSTRACT
With the world experiencing a paradigm shift in its working culture and people working from home, while it's been beneficial to many industries helping them with cost-cutting, some have suffered irrationally at the hands of COVID-19, and for them, employees returning to site has become the need of the hour, with lockdown rules easing out and new strains of viruses being discovered, utmost precaution needs to be taken. Hence there is a need for a comprehensive system that can ensure proper protocols being followed as well as manage the office workloads for each and every employee which is what we’ll be discussing about in detail, the various libraries, tools, frameworks, machine learning algorithms, sensors used to realize a Zero Human Contact cost-efficient IoT-Machine learning-enabled setup capable of managing the whole office and ensure a safe working environment for its staff. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.