Identifying fatigue of climbing workers using physiological data based on the XGBoost algorithm
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...
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| Published in | Frontiers in public health Vol. 12; p. 1462675 |
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| Main Authors | , , , , , , , |
| Format | Journal Article |
| Language | English |
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09.10.2024
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| Abstract | 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 (SpO
), 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 SpO
, 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. |
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| AbstractList | BackgroundHigh-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.MethodsThis 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.ResultsThe 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%.ConclusionThis 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. 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 (SpO ), 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 SpO , 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. 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.BackgroundHigh-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.MethodsThis 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%.ResultsThe 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.ConclusionThis 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. |
| Author | Zhu, Kunshuang Hou, Wei Xu, Yonggang Cui, Kai Jian, Qingzhi Wang, Mingjun Gong, Zichao Xu, Mingkai |
| AuthorAffiliation | 2 State Grid Shandong Electric Power Company , Jinan , China 1 Emergency Management Center of State Grid Shandong Electric Power Company , Jinan , China 3 School of Modern Postal, Xi'an University of Posts and Telecommunications , Xi'an , China |
| AuthorAffiliation_xml | – name: 1 Emergency Management Center of State Grid Shandong Electric Power Company , Jinan , China – name: 2 State Grid Shandong Electric Power Company , Jinan , China – name: 3 School of Modern Postal, Xi'an University of Posts and Telecommunications , Xi'an , China |
| Author_xml | – sequence: 1 givenname: Yonggang surname: Xu fullname: Xu, Yonggang – sequence: 2 givenname: Qingzhi surname: Jian fullname: Jian, Qingzhi – sequence: 3 givenname: Kunshuang surname: Zhu fullname: Zhu, Kunshuang – sequence: 4 givenname: Mingjun surname: Wang fullname: Wang, Mingjun – sequence: 5 givenname: Wei surname: Hou fullname: Hou, Wei – sequence: 6 givenname: Zichao surname: Gong fullname: Gong, Zichao – sequence: 7 givenname: Mingkai surname: Xu fullname: Xu, Mingkai – sequence: 8 givenname: Kai surname: Cui fullname: Cui, Kai |
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| Keywords | climbing workers machine learning XGBoost fatigue identification physiological data |
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| SubjectTerms | Adult Algorithms Blood Pressure - physiology climbing workers Fatigue fatigue identification Female Hand Strength - physiology Heart Rate - physiology Humans machine learning Male physiological data Public Health Reaction Time - physiology XGBoost Young Adult |
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| Title | Identifying fatigue of climbing workers using physiological data based on the XGBoost algorithm |
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