A Study on Fight Against COVID-19 from Latest Technological Intervention

Arulprakash, Enoch; Aruldoss, Martin

doi:10.1007/s42979-020-00301-0

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…Technological intervention is crucial and significant to reduce COVID-19 transmissions. Artificial intelligence (AI) technology is exploited to analyze aspects of the pandemic from contact tracing, early detection, and treatment [8]. Besides, AI was also employed to monitor social distancing violations in crowded areas and to detect whether people were wearing face masks or not.…”

Section: Introductionmentioning

confidence: 99%

A Simple and Cost-Effective Physical Distancing Violation Detector Using a Rotating Time of Flight Lidar

Hanto¹,

Adinanta²,

Suryadi³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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In this work, a simple and cost-effective physical distancing violation detector using a commercial lidar has been developed. Our system comprises time of flight (ToF) lidar, mounted a stepper motor to rotate ToF Lidar and range an object on the top. We control a rotation of the stepper motor, record the distance between the object and the ToF Lidar by using a microcontroller, and analyze the measuring data using a computer program. This system can also indirectly estimate the distance between two objects by applying a simple vector operation. This paper successfully detects and evaluates the distance between two dummy objects placed with various configurations. We obtained the estimated distances using our proposed method nearly equal to the actual distances measured manually. In addition, our system has been tested to measure the physical distances among people with three volunteers who stood 200 cm and 80 cm distances in an indoor environment. The experiment results show that the distance between volunteer 1 and volunteer 2 is 186.5 cm and the distance between volunteer 2 and volunteer 3 is 73.0 cm. These indicate our system could provide information whether a safe distance or a risk distance. This research work can help the authorities provide an instrument for reducing contagious diseases, especially COVID-19 pandemic outbreaks, by installing at a fixed location or in portable instrument services.

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Section: Introductionmentioning

confidence: 99%

A Simple and Cost-Effective Physical Distancing Violation Detector Using a Rotating Time of Flight Lidar

Hanto¹,

Adinanta²,

Suryadi³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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show abstract

“…Therefore, the fight against COVID-19 is unstoppable, to safeguard human life until the exact vaccine is discovered. AI/Deep learning can enhance and respond to the situation, through inspecting different aspects of the pandemic [3]. In terms of COVID-19 nature, which is an acronym for Coronavirus Disease 2019, it is a respiratory disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).…”

Section: Introductionmentioning

confidence: 99%

Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT

Alsaydia¹,

Saadallah²,

Malallah³

et al. 2021

EEJET

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During the current outbreak of the COVID-19 pandemic, controlling and decreasing the possibilities of infections are massively required. One of the most important solutions is to use Artificial Intelligence (AI), which combines both fields of deep learning (DL) and the Internet of Things (IoT). The former one is responsible for detecting any face, which is not wearing a mask. Whereas, the latter is exploited to manage the control for the entire building or a public area such as bus, train station, or airport by connecting a Closed-Circuit Television (CCTV) camera to the room of management. The work is implemented using a Core-i5 CPU workstation attached with a Webcam. Then, MATLAB software is programmed to instruct both Arduino and NodeMCU (Micro-Controller Unit) for remote control as IoT. In terms of deep learning, a 15-layer convolutional neural network is exploited to train 1,376 image samples to generate a reference model to use for comparison. Before deep learning, preprocessing operations for both image enhancement and scaling are applied to each image sample. For the training and testing of the proposed system, the Simulated Masked Face Recognition Dataset ( SMFRD) has been exploited. This dataset is published online. Then, the proposed deep learning system has an average accuracy of up to 98.98 %, where 80 % of the dataset was used for training and 20 % of the samples are dedicated to testing the proposed intelligent system. The IoT system is implemented using Arduino and NodeMCU_TX (for transmitter) and RX (for receiver) for the signal transferring through long distances. Several experiments have been conducted and showed that the results are reasonable and thus the model can be commercially applied

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“…it also has the potential of identifying trigger images. These trigger images may include crowds breaching social distancing guidelines and the presence of a quarantine subject in a public area [51]. CCTV and drone footage can provide a source of video surveillance data necessary to limit virus spread by human presence in certain areas [20], [36].…”

mentioning

confidence: 99%

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution

et al. 2020

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The novel coronavirus (COVID-19), declared by the World Health Organization (WHO) as a global pandemic, has brought with it changes to the general way of life. Major sectors of the world industry and economy have been affected and the Internet of Things (IoT) management and framework is no exception in this regard. This article provides an up to date survey on how a global pandemic such as COVID-19 has affected the world of IoT technologies. It looks at the contributions that IoT and associated sensor technologies have made towards virus tracing, tracking and spread mitigation. The associated challenges of deployment of sensor hardware in the face of a rapidly spreading pandemic have been looked into as part of this review article. The effects of a global pandemic on the evolution of IoT architectures and management have also been addressed, leading to the likely outcomes on future IoT implementations. In general, this article provides an insight into the advancement of sensor-based E-health towards the management of global pandemics. It also answers the question of how a global virus pandemic has shaped the future of IoT networks.

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A Study on Fight Against COVID-19 from Latest Technological Intervention

Cited by 7 publications

References 14 publications

A Simple and Cost-Effective Physical Distancing Violation Detector Using a Rotating Time of Flight Lidar

A Simple and Cost-Effective Physical Distancing Violation Detector Using a Rotating Time of Flight Lidar

Limiting COVID-19 infection by automatic remote face mask monitoring and detection using deep learning with IoT

IoT in the Wake of COVID-19: A Survey on Contributions, Challenges and Evolution

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