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ORIGINAL RESEARCH article

Front. Public Health
Sec. Occupational Health and Safety
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1462675
This article is part of the Research Topic Machine Learning and Deep Learning in Data Analytics and Predictive Analytics of Physiological Data View all articles

Identifying Fatigue of Climbing Workers Using Physiological Data Based on The XGBoost Algorithm

Provisionally accepted
Yonggang Xu Yonggang Xu 1Qingzhi Jian Qingzhi Jian 2*Kunshuang Zhu Kunshuang Zhu 1*Mingjun Wang Mingjun Wang 1*Wei Hou Wei Hou 1*Zichao Gong Zichao Gong 1*Mingkai Xu Mingkai Xu 2*Kai Cui Kai Cui 3*
  • 1 Emergency Management Center of State Grid Shandong Electric Power Company, Jinan, China
  • 2 State Grid Shandong Electric Power Company, Jinan, Shandong Province, China
  • 3 School of Modern Postal, Xi’an University of Posts and Telecommunications, Xi'an, China

The final, formatted version of the article will be published soon.

    Background: High-voltage workers often experience fatigue due to the physically demanding nature of climbing in dynamic and complex environments, which negatively impacts their motor and mental abilities. Effective monitoring is necessary to ensure safety.This study proposed an experimental method to quantify fatigue in climbing operations. We collected subjective fatigue (using the RPE scale) and objective fatigue data, including systolic blood pressure (SBP), diastolic blood pressure (DBP), blood oxygen saturation (SpO2), vital capacity (VC), grip strength (GS), response time (RT), critical fusion frequency (CFF), and heart rate (HR) from 33 high-voltage workers before and after climbing tasks. The XGBoost algorithm was applied to establish a fatigue identification model.The analysis showed that the physiological indicators of SpO2, VC, GS, RT, and CFF can effectively evaluate fatigue in climbing operations. The XGBoost fatigue identification model, based on subjective fatigue and the five physiological indicators, achieved an average accuracy of 89.75%.This study provides a basis for personalized management of fatigue in climbing operations, enabling timely detection of their fatigue states and implementation of corresponding measures to minimize the likelihood of accidents.

    Keywords: Fatigue identification, climbing workers, Physiological data, machine learning, XGBoost

    Received: 12 Jul 2024; Accepted: 27 Sep 2024.

    Copyright: © 2024 Xu, Jian, Zhu, Wang, Hou, Gong, Xu and Cui. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Qingzhi Jian, State Grid Shandong Electric Power Company, Jinan, 250001, Shandong Province, China
    Kunshuang Zhu, Emergency Management Center of State Grid Shandong Electric Power Company, Jinan, China
    Mingjun Wang, Emergency Management Center of State Grid Shandong Electric Power Company, Jinan, China
    Wei Hou, Emergency Management Center of State Grid Shandong Electric Power Company, Jinan, China
    Zichao Gong, Emergency Management Center of State Grid Shandong Electric Power Company, Jinan, China
    Mingkai Xu, State Grid Shandong Electric Power Company, Jinan, 250001, Shandong Province, China
    Kai Cui, School of Modern Postal, Xi’an University of Posts and Telecommunications, Xi'an, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.