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Hypoxia Detection for Confined-Space Workers: Photoplethysmography and Machine-Learning Techniques

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Abstract

Oxygen-deficiency is a cause of fatalities in confined-space workplaces. Most research and projects have been conducted to reduce work-related accidents by external measurements, while works addressing the early warning of workers’ hypoxia state through a bio-electrical signal have rarely been conducted. In this paper, we present a hypoxia detection system based on non-invasive photoplethysmograph (PPG) measurement using machine-learning (ML) algorithms. The PPG signals obtained from 22 subjects underwent preprocessing and features extraction steps. Time-domain features, rising and falling slopes, and amplitude parameters were adopted to train and test the ML algorithms. In addition, an Internet of Things- (IoT) based smart wearable device and monitoring system were developed to measure the vital parameters of workers in confined spaces. Cardiac cycle time of all the participants except subject 8 decreased significantly (P < 0.01) throughout the oxygen-deficiency trial. The PPG signal complexity essentially decreased (P = 0.006) when the concentration of environmental oxygen declined. The DT algorithm with nine input parameters outperformed the other algorithms in prediction accuracy (Acc = 0.9). The results show that the features extracted from the PPG signal can be adopted as important indicators to revealing the hypoxia state of workers. The device and system automatically measure and analyze the PPG signal with the location details of caregivers and notify the monitoring staff in the case of an emergency.

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Availability of Data and Materials

The datasets generated during the current study are available from the corresponding author on reasonable request.

Code Availability

The custom code developed in this study is available from the corresponding author on reasonable request.

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Acknowledgements

The author would like to thank the participants and research assistants for their time and commitment during this study.

Funding

This work was supported by the Fundamental Research Funds for the Central Universities (No. FRF-IDRY-19-009, No. FRF-TP-19-038A1).

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Authors and Affiliations

  1. School of Civil Engineering and Resources, University of Science and Technology Beijing, Beijing, 100083, People’s Republic of China

    Yixuan Wei, Longzhe Jin, Shu Wang, Yifei Xu & Tianqi Ding

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  1. Yixuan Wei
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  2. Longzhe Jin
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  3. Shu Wang
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  4. Yifei Xu
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Contributions

YW, LJ, SW, YX, and TD contributed to the design and implementation of the research, to the analysis of the results and to the writing of the manuscript.

Corresponding author

Correspondence to Longzhe Jin.

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The authors declare no conflict of interest related to the material presented in this article. Its contents, including any opinions and/or conclusions expressed, are solely those of the authors.

Ethical Approval

The study was approved by the University of Science and Technology Beijing Review Board (No. 2021-1-106).

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The participants signed the informed consent.

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Written informed consent for publication was obtained from all the participants.

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Wei, Y., Jin, L., Wang, S. et al. Hypoxia Detection for Confined-Space Workers: Photoplethysmography and Machine-Learning Techniques. SN COMPUT. SCI. 3, 290 (2022). https://doi.org/10.1007/s42979-022-01162-5

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