ArticleFrontiers in public health2024
Identifying fatigue of climbing workers using physiological data based on the XGBoost algorithm.
Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Study on mental fatigue detection of crane operators based on frequency and channel attention spiking neural network with multivariate variational mode decomposition in real construction environments.Medical & biological engineering & computing · 2026Article
- Capacitance-Based Untethered Fatigue Driving Recognition Under Various Light Conditions.Sensors (Basel, Switzerland) · 2024Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. Methods: 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 Results: The analysis showed that the physiological indicators of SpO Conclusion: 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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