COVID19-Routes: A Safe Pedestrian Navigation Service

Cantarero, Ruben; Rubio, Ana; Santofimia, María J.; Dorado, Javier Hernán; Fernandez, J Delgado; López, Juan Carlos

doi:10.1109/access.2021.3092970

Cited by 4 publications

(2 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In Table 1, we examine recent studies investigating the navigation and route recommendation for users while exploiting the IoT data to help on mitigating the spread of infection, especially COVID-19 [26]- [30]. We found that majority of selected studies focus exclusively on indoor navigation, while the other concern only on out-door navigation.…”

Section: Related Workmentioning

confidence: 99%

An End-to-End Smart IoT-Driven Navigation for Social Distancing Enforcement

et al. 2022

View full text Add to dashboard Cite

The unprecedented global spread of the coronavirus pandemic COVID-19 has significantly promoted novel Internet-of-things (IoT)-based solutions to prevent, combat, monitor, or predict virus spread in the population. The proliferation of these technologies has fostered their utilization for different practical use-cases to offer reinforced control, discipline, and safety. This paper proposes an end-to-end smart navigation framework that uses Social IoT (SIoT) and Artificial Intelligence (AI) techniques to ensure pedestrians' navigation safety through a given geographical area. The aim is to mitigate the risks of exposure to the virus and impose social distancing practices while avoiding high-risk areas identified from the SIoT data. First, we create weighted graphs representing the social relations connecting the different IoT devices in the area of interest. Second, we regroup the devices into communities according to their SIoT relations that consider their locations and owners' friendship levels. Next, we extract CCTV recorded videos to estimate the level of social distancing practice on different roads using a computer vision model. Accordingly, the road segments are assigned weights representing their safety levels based on the extracted data. Afterward, a graph-based routing algorithm is executed to recommend the route to follow while achieving a trade-off between speed and safety. Finally, the proposed framework is generalized to enable multi-user coordinated navigation. The feasibility of the proposed approach on real-world maps and IoT datasets is corroborated in our simulation results showing an ability to balance safety and travel distance, which can be adjusted according to the user's preferences.

show abstract

Section: Related Workmentioning

confidence: 99%

An End-to-End Smart IoT-Driven Navigation for Social Distancing Enforcement

et al. 2022

View full text Add to dashboard Cite

show abstract

“…SARS-CoV-2 or COVID-19 is the worldwide epidemic caused by Coronavirus illness in 2019 caused severe acute respiratory syndrome (SARS) and was found for the first time in Wuhan, which is located in China. In March 2020, the World Health Organization (WHO) gave the disease the name COVID-19 and predicted that it would spread over the world as an epidemic (Cantarero et al 2021;De Anda-Suarez et al 2022). COVID-19 negatively affects the health of people, especially the elderly, people with existing medical issues, and people whose immune systems are already impaired.…”

Section: Introduction 11 Overviewmentioning

confidence: 99%

Classified Covid-19 by Densenet121-Based Deep Transfer Learning From Ct-Scan Images

Ibrahim,

Mahmood

2023

SJUOZ

View full text Add to dashboard Cite

The COVID-19 disease, which has recently emerged and has been considered a worldwide pandemic, has had a significant impact on the lives of millions of people and has forced a substantial load on healthcare organizations. Numerous deep-learning models have been utilized for diagnosing coronaviruses from chest computed tomography (CT) images. However, in light of the limited availability of datasets on COVID-19, the pre-trained deep learning networks were used. The main objective of this research is to construct and develop an automated approach for the early detection and diagnosis of COVID-19 in thoracic CT images. This paper proposes the DDTL-COV model, a deep transfer learning model based on DenseNet121, to classify patients on CT scans as either COVID or non-COVID, utilizing weights obtained from the ImageNet dataset. Two datasets were used to train the DDTL-COV model: the SARS-CoV-2 CT-scan dataset and the COVID19-CT dataset. In the SARS-CoV-2 CT dataset, the model achieved a good accuracy of 99.6%. However, on the second dataset (COVID19-CT dataset), its performance shows an accuracy rate of 89%. These results show that the model performed better than alternative methods.

show abstract