Face Masks Usage Monitoring for Public Health Security using Computer Vision on Hardware

Kolosov, Dimitrios; Mporas, Iosif

doi:10.1109/iccst49569.2021.9717402

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…Consequently, in the context of developing systems targeted for the edge, it is imperative to comprehend the trade-offs between the quality of the models, computational costs, commercial costs, and power consumption. In [35,36], several computer vision models are evaluated and compared to six popular edge devices in terms of accuracy and inference time.…”

Section: Health Monitoring On Edge Hardwarementioning

confidence: 99%

Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware

Kolosov

Kelefouras

Kourtessis

et al. 2023

Sensors

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Detecting vital signs by using a contactless camera-based approach can provide several advantages over traditional clinical methods, such as lower financial costs, reduced visit times, increased comfort, and enhanced safety for healthcare professionals. Specifically, Eulerian Video Magnification (EVM) or Remote Photoplethysmography (rPPG) methods can be utilised to remotely estimate heart rate and respiratory rate biomarkers. In this paper two contactless camera-based health monitoring architectures are developed using EVM and rPPG, respectively; to this end, two different CNNs, (Mediapipe’s BlazeFace and FaceMesh) are used to extract suitable regions of interest from incoming video frames. These two methods are implemented and deployed on four off-the-shelf edge devices as well as on a PC and evaluated in terms of latency (in each stage of the application’s pipeline), throughput (FPS), power consumption (Watt), efficiency (throughput/Watt), and value (throughput/cost). This work provides important insights about the computational costs and bottlenecks of each method on each hardware platform, as well as which platform to use depending on the target metric. One of our insights shows that the Jetson Xavier NX platform is the best platform in terms of throughput and efficiency, while Raspberry Pi 4 8 GB is the best platform in terms of value.

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Section: Health Monitoring On Edge Hardwarementioning

confidence: 99%

Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware

Kolosov

Kelefouras

Kourtessis

et al. 2023

Sensors

Self Cite

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“…Moreover, the machine learning modelling and estimation analysis are running offline on a computer, except for [19] presenting a handheld spectroscopic device with onboard statistical data analysis, but not machine learning-based analysis. The recent development of powerful microprocessors and embedded systems has allowed the development of solutions which run the artificial intelligence algorithms on the edge device, with the most popular applications of 'AI on edge' being in computer vision [20,21].…”

Section: Introductionmentioning

confidence: 99%

Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems

Kolosov

Fengou

Carstensen

et al. 2023

Sensors

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Spectroscopic sensor imaging of food samples meta-processed by deep machine learning models can be used to assess the quality of the sample. This article presents an architecture for estimating microbial populations in meat samples using multispectral imaging and deep convolutional neural networks. The deep learning models operate on embedded platforms and not offline on a separate computer or a cloud server. Different storage conditions of the meat samples were used, and various deep learning models and embedded platforms were evaluated. In addition, the hardware boards were evaluated in terms of latency, throughput, efficiency and value on different data pre-processing and imaging-type setups. The experimental results showed the advantage of the XavierNX platform in terms of latency and throughput and the advantage of Nano and RP4 in terms of efficiency and value, respectively.

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Face Masks Usage Monitoring for Public Health Security using Computer Vision on Hardware

Cited by 2 publications

References 15 publications

Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware

Contactless Camera-Based Heart Rate and Respiratory Rate Monitoring Using AI on Hardware

Microbiological Quality Estimation of Meat Using Deep CNNs on Embedded Hardware Systems

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