K-means cluster output.

<div><p>Firefighting operations in high-rise building fires require firefighters to navigate complex environments while undertaking physically demanding, heavy-load tasks, which often lead to severe fatigue, impairing their operational efficiency and decision-making. This study aims to d...

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Bibliographic Details
Main Author:	Mingwei Xu (9548454) (author)
Other Authors:	Shangxue Yang (21370261) (author), Ke Wang (82395) (author), Chengliu Yu (21370264) (author), Guanlin Liu (1437838) (author), Chao Dai (316953) (author), Ruiqi Wang (354713) (author)
Published:	2025
Subjects:	Medicine Molecular Biology Developmental Biology Biological Sciences not elsewhere classified Information Systems not elsewhere classified undertaking physically demanding r &# 178 navigate complex environments firefighter &# 8217 fire rescue missions established using k entropy weight analysis bp neural network 6 &# 8211 24 %, demonstrating 10 features derived subjective fatigue ratings comprehensive fatigue index reaction time data enhancing operational effectiveness tier fatigue classification subjective fatigue levels robust fatigue classification level fatigue classification heart rate variability f ), enabling dynamic fatigue prediction high prediction accuracy fatigue levels heart rate severe fatigue fatigue states reaction times operational efficiency prediction model significant potential scientific basis rpe scale often lead metrics serving means clustering load tasks key metrics including electrocardiogram findings highlight approach provides
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K-means cluster output.

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