IoT for COVID-19 Indoor Spread Prevention: Cough Detection, Air Quality Control and Contact Tracing

Petrović, Nenad; Kocić, Đorđe

doi:10.1109/miel52794.2021.9569099

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Zhang et al [522] have integrated genetic algorithms and artificial neural networks to control ventilation actions and improve IAQ indices. To mitigate indoor infection risks associated with COVID-19, information-based technologies have been developed, such as social distance monitoring [523,524], cough detection [525], and the use of robots for disinfection [526,527].…”

Section: Applications With Iaq Datamentioning

confidence: 99%

Review of Engineering Controls for Indoor Air Quality: A Systems Design Perspective

2023

Sustainability

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This paper aims to review the engineering controls for indoor air quality (IAQ) from a systems design perspective. As a result of the review, we classify the literature content into three categories: (1) indoor air treatments, (2) dissemination control strategies, and (3) information technology. Indoor air treatments can be generally interpreted as the “cleaning” aspect, which covers ventilation and contaminant removal techniques. Dissemination control focuses on how contaminants generated in an indoor space can be transmitted, where four types of dissemination are classified. The category of information technology discusses IAQ sensors for monitoring, as well as the applications of the Internet of Things and IAQ data. Then, we further analyze the reviewed engineering controls by performing systems and functional analysis. Along with a discussion of IAQ functions, we suggest some systems design techniques, such as functional decoupling and design for flexibility/resilience, which are expected to promote more systems thinking in designing IAQ solutions.

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Section: Applications With Iaq Datamentioning

confidence: 99%

Review of Engineering Controls for Indoor Air Quality: A Systems Design Perspective

2023

Sustainability

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“…TinyML opens up a broad spectrum of real-time and lowfootprint eHealth applications, some of which are summarized in Table XVI. These include monitoring eating episodes and coughs using microphones [221], [223], sleep monitoring and arrhythmia detection through ECG measurements [171], [222], epileptic seizure recognition from EEG sensors [171], and fall detection using earable inertial sensors [2]. Most TinyML mHealth applications are variants of anomaly detection, indicating the presence or the absence of a health condition, thereby allowing the use of ultralightweight models in the order of 10 0 -10 1 kB.…”

Section: F Mhealthmentioning

confidence: 99%

“…Most TinyML mHealth applications are variants of anomaly detection, indicating the presence or the absence of a health condition, thereby allowing the use of ultralightweight models in the order of 10 0 -10 1 kB. Example models for mHealth include Bonsai [2], embedded GRU [221], 1-D CNN [222], FC-AE [171], and two-layer CNN/LSTM [223].…”

Section: F Mhealthmentioning

confidence: 99%

Machine Learning for Microcontroller-Class Hardware: A Review

2022

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The advancements in machine learning (ML) opened a new opportunity to bring intelligence to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML deployment has high memory and computes footprint hindering their direct deployment on ultraresource-constrained microcontrollers. This article highlights the unique requirements of enabling onboard ML for microcontroller-class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure that the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of ML model development for microcontroller-class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.

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“…Many additional papers focused on cellular or WiFi networks have surfaced during the COVID-19 pandemic [1, 9, 11, 15, 22, 23, 26-28, 30, 32, 42, 45-48, 50, 52] which do not include ground truth datasets. Overview articles that explore WiFi technologies include [4,7,8,[33][34][35]38].…”

Section: Related Workmentioning

confidence: 99%

Network-Side Digital Contact Tracing on a Large University Campus

Malloy¹,

Hartung²,

Wangen³

et al. 2022

Preprint

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We describe a study conducted at a large public university campus in the United States which shows the efficacy of network log information for digital contact tracing and prediction of COVID-19 cases. Over the period of January 18, 2021 to May 7, 2021, more than 216 million client-access-point associations were logged across more than 11,000 wireless access points (APs). The association information was used to find potential contacts for approximately 30,000 individuals. Contacts are determined using an AP colocation algorithm, which supposes contact when two individuals connect to the same WiFi AP at approximately the same time. The approach was validated with a truth set of 350 positive COVID-19 cases inferred from the network log data by observing associations with APs in isolation residence halls reserved for individuals with a confirmed (clinical) positive COVID-19 test result. The network log data and AP-colocation have a predictive value of greater than 10%; more precisely, the contacts of an individual with a confirmed positive COVID-19 test have greater than a 10% chance of testing positive in the following 7 days (compared with a 0.79% chance when chosen at random, a relative risk ratio of 12.6). Moreover, a cumulative exposure score is computed to account for exposure to multiple individuals that test positive. Over the duration of the study, the cumulative exposure score predicts positive cases with a true positive rate of 16.5% and missed detection rate of 79% at a specified operating point.

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IoT for COVID-19 Indoor Spread Prevention: Cough Detection, Air Quality Control and Contact Tracing

Cited by 7 publications

References 8 publications

Review of Engineering Controls for Indoor Air Quality: A Systems Design Perspective

Review of Engineering Controls for Indoor Air Quality: A Systems Design Perspective

Machine Learning for Microcontroller-Class Hardware: A Review

Network-Side Digital Contact Tracing on a Large University Campus

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