Reducing environment exposure to COVID-19 by IoT sensing and computing with deep learning

The COVID-19 pandemic has caused significant harm globally, prompting us to prioritize prevention measures. Effective hand-washing is one of the most critical and straightforward measures that can help prevent the spread of this virus. Medical staff’s hands are considered a major source of hospital...

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Published in:Neural computing & applications Vol. 35; no. 36; pp. 25097 - 25106
Main Authors: Ma, Chendong, Song, Jun, Xu, Yibo, Fan, Hongwei, Liu, Xiaoran, Wu, Xing, Luo, Yang, Sun, Tuo, Xie, Jiemin
Format: Journal Article
Language:English
Published: London Springer London 01.12.2023
Springer Nature B.V
Subjects:
Artificial Intelligence
Computational Biology/Bioinformatics
Computational grids
Computational Science and Engineering
Computer Science
Computer vision
COVID-19
Data Mining and Knowledge Discovery
Deep learning
Disease control
Edge computing
Hospitals
Image processing
Image Processing and Computer Vision
Pattern recognition
Prevention
Probability and Statistics in Computer Science
Real time
S.I.: Evolutionary Computation based Methods and Applications for Data Processing
Special Issue on Evolutionary Computation based Methods and Applications for Data Processing
Viral diseases
Viruses
Washing
COVID-19
Environment exposure
Identification framework
Edge-computing
Deep learning network
ISSN:0941-0643, 1433-3058
Online Access:Get full text
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Summary:The COVID-19 pandemic has caused significant harm globally, prompting us to prioritize prevention measures. Effective hand-washing is one of the most critical and straightforward measures that can help prevent the spread of this virus. Medical staff’s hands are considered a major source of hospital infection. Effective hand-washing can prevent up to 30% of diarrhea-related illnesses, which is crucial in preventing nosocomial infections (Tartari et al. in Clin Microbiol Infect 23(9):596–598, 2017). This paper proposes an electronic-based real-time hand-washing identification framework called Alpha Hand Washing (ALPHA HW) . The system uses camera-based identification, edge computing, and deep learning to automatically identify correct hand-washing behaviors, thereby facilitating effective hand-washing (Bertasius et al. in: Computer vision and pattern recognition, 2015). We achieved an accuracy of 78.0% mAP and a speed of 52 FPS in detecting scenes using specific monitoring datasets in hospitals by constructing the complex recognition system into a grid computing problem. Leveraging edge computing, our system achieves real-time identification with low memory consumption and high-efficiency computation. Alpha HW presents scientific and financial values in epidemic prevention and control that can facilitate popularization to reduce virus spread (Bewley et al. in 2016 IEEE international conference on image processing, 2016).
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ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-023-08712-9