Smart technologies for COVID-19 indoor monitoring

Petrović, Nenad; Kocić, Đorđe

doi:10.1016/b978-0-323-85206-7.00012-5

Cited by 16 publications

(16 citation statements)

References 17 publications

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“…Low-power binary NN-based COVID-19 FMD, such as BinaryCoP [202], can be employed to identify correct facial mask wearing and positioning using edge computing and mobile devices. Another solution is to use light-weight CNN models, such that in [203], for performing multiple access control-related tasks, including the detection of face masks, the monitoring of body temperature, and the counting of people entering a building or an area (e.g., a concert hall). Such models can be trained offline and used for inference on singleboard computers (SBCs) such as Raspberry Pi, Jetson Nano, or others.…”

Section: Computational Costmentioning

confidence: 99%

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Himeur

Al-Maadeed

Varlamis

et al. 2023

Systems

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After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail.

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Section: Computational Costmentioning

confidence: 99%

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Himeur

Al-Maadeed

Varlamis

et al. 2023

Systems

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“…Others have worked on tools to provide the real-time validation of COVID-19 safety and check whether certain guidelines have been respected. Petrović and Kocić [15] created an IoT architecture that checks social distancing and the wearing of masks with a camerabased system, while also using semantic technologies to link all data. Numerous other literature for social distance validation is available, for example [16,17].…”

Section: Covid-19 Safetymentioning

confidence: 99%

Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings

Vanhaeverbeke

Deprost

Bonte

et al. 2023

Sensors

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A healthy and safe indoor environment is an important part of containing the coronavirus disease 2019 (COVID-19) pandemic. Therefore, this work presents a real-time Internet of things (IoT) software architecture to automatically calculate and visualize a COVID-19 aerosol transmission risk estimation. This risk estimation is based on indoor climate sensor data, such as carbon dioxide (CO2) and temperature, which is fed into Streaming MASSIF, a semantic stream processing platform, to perform the computations. The results are visualized on a dynamic dashboard that automatically suggests appropriate visualizations based on the semantics of the data. To evaluate the complete architecture, the indoor climate during the student examination periods of January 2020 (pre-COVID) and January 2021 (mid-COVID) was analyzed. When compared to each other, we observe that the COVID-19 measures in 2021 resulted in a safer indoor environment.

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“…A plethora of digital technologies are being used to enhance practices of monitoring and tracing, via small sensors on the individual or at key gateways of disease transmission to generate static or dynamic maps of disease movement. For example, automated counters at store entrances inform visitors of how many customers are currently in the store and if it is at a level that is deemed to be safe (Khoumeri et al 2021;Petrović & Kocić 2022). Continued access and use of public urban spaces is possible during pandemics through the implementation and uptake of physical-digital hybrid distancing (i.e.…”

Section: Changes In Usementioning

confidence: 99%

Pandemics and the built environment: A human–building interaction typology

Vallis

Karvonen

Eriksson

2023

Buildings and Cities

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Surveys of urban history from ancient times to the present reveal a continuum of collective responses to pandemics ranging from quarantine facilities and monitoring the spread of disease to building new wastewater networks. The contemporary COVID-19 pandemic includes new digital tools and techniques that supplement (and sometimes replace) the existing analogue responses, while raising new ethical issues with respect to privacy. A typology of pandemic responses in cities is created, based on human-building interaction (HBI) principles. This typology can be used to compare and contrast analogue and digital responses relating to distancing, monitoring and sanitising. It provides a summary of a wide range of individual and collective implications of pandemics and demonstrates the indelible connections between pandemics and the built environment. In addition, the typology provides a tool to interpret some of the opportunities and drawbacks of digitalising cities.

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Smart technologies for COVID-19 indoor monitoring

Cited by 16 publications

References 17 publications

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Face Mask Detection in Smart Cities Using Deep and Transfer Learning: Lessons Learned from the COVID-19 Pandemic

Real-Time Estimation and Monitoring of COVID-19 Aerosol Transmission Risk in Office Buildings

Pandemics and the built environment: A human–building interaction typology

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