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

  • S.I.: Evolutionary Computation based Methods and Applications for Data Processing
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Abstract

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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Funding

The work is supported by the Smart Society Lab at Hong Kong Baptist University.

Author information

Authors and Affiliations

  1. Department of Geography, Hong Kong Baptist University, Kowloon Tong, China

    Chendong Ma & Jun Song

  2. Smart Society Lab, Hong Kong Baptist University, Kowloon Tong, China

    Jun Song

  3. Zhilun Technology Research Institute, Nanjing, China

    Yibo Xu

  4. Department of Natural Sciences, Imperial College London, London, UK

    Hongwei Fan

  5. Department of Electrical and Electronic Engineering, Imperial College London, London, UK

    Xiaoran Liu

  6. School of Computer Engineering and Science, Shanghai University, Shanghai, China

    Xing Wu

  7. Department of Computer Science, University of Electronic and Technology, Chengdu, China

    Yang Luo

  8. School of Traffic, Tongji University, Shanghai, China

    Tuo Sun

  9. Department of Civil Engineering, The University of Hong Kong, Hong Kong, China

    Jiemin Xie

Authors
  1. Chendong Ma
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  2. Jun Song
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  3. Yibo Xu
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  4. Hongwei Fan
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  5. Xiaoran Liu
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  6. Xing Wu
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  7. Yang Luo
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  8. Tuo Sun
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  9. Jiemin Xie
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Corresponding author

Correspondence to Jun Song.

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Ma, C., Song, J., Xu, Y. et al. Reducing environment exposure to COVID-19 by IoT sensing and computing with deep learning. Neural Comput & Applic 35, 25097–25106 (2023). https://doi.org/10.1007/s00521-023-08712-9

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  • DOI: https://doi.org/10.1007/s00521-023-08712-9

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