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...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , |
| منشور في: |
2025
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1852020355417243648 |
|---|---|
| author | Mingwei Xu (9548454) |
| author2 | Shangxue Yang (21370261) Ke Wang (82395) Chengliu Yu (21370264) Guanlin Liu (1437838) Chao Dai (316953) Ruiqi Wang (354713) |
| author2_role | author author author author author author |
| author_facet | Mingwei Xu (9548454) Shangxue Yang (21370261) Ke Wang (82395) Chengliu Yu (21370264) Guanlin Liu (1437838) Chao Dai (316953) Ruiqi Wang (354713) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mingwei Xu (9548454) Shangxue Yang (21370261) Ke Wang (82395) Chengliu Yu (21370264) Guanlin Liu (1437838) Chao Dai (316953) Ruiqi Wang (354713) |
| dc.date.none.fl_str_mv | 2025-05-15T18:15:26Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0323911.g003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/K-means_cluster_output_/29081191 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | K-means cluster output. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <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 develop a robust fatigue classification and prediction model to assess and forecast firefighters’ fatigue levels. Key metrics, including electrocardiogram (ECG) signals, subjective fatigue ratings, and reaction time data, were utilized. Experiments involving six healthy adult male participants simulated firefighting scenarios, during which subjective fatigue levels (6–20 Borg’s RPE scale) and reaction times were recorded. A five-level fatigue classification was established using K-means clustering, and entropy weight analysis was applied to define a comprehensive fatigue index (F), enabling a three-tier fatigue classification: light, moderate, and severe fatigue. A BP neural network was employed for dynamic fatigue prediction, with 10 features derived from heart rate and heart rate variability (HRV) metrics serving as inputs and the comprehensive fatigue index (F) as the output. The BP neural network model achieved a high prediction accuracy with an R² value of 93.24%, demonstrating its capability to accurately predict firefighters’ fatigue states. This approach provides a scientific basis for optimizing firefighter training protocols and enhancing operational effectiveness during fire rescue missions. The findings highlight the significant potential of this method for advancing firefighter fatigue monitoring and management.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_d119aadfeeb0dfe61cabc2e792d2d261 |
| identifier_str_mv | 10.1371/journal.pone.0323911.g003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/29081191 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | K-means cluster output.Mingwei Xu (9548454)Shangxue Yang (21370261)Ke Wang (82395)Chengliu Yu (21370264)Guanlin Liu (1437838)Chao Dai (316953)Ruiqi Wang (354713)MedicineMolecular BiologyDevelopmental BiologyBiological Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedundertaking physically demandingr &# 178navigate complex environmentsfirefighter &# 8217fire rescue missionsestablished using kentropy weight analysisbp neural network6 &# 821124 %, demonstrating10 features derivedsubjective fatigue ratingscomprehensive fatigue indexreaction time dataenhancing operational effectivenesstier fatigue classificationsubjective fatigue levelsrobust fatigue classificationlevel fatigue classificationheart rate variabilityf ), enablingdynamic fatigue predictionhigh prediction accuracyfatigue levelsheart ratesevere fatiguefatigue statesreaction timesoperational efficiencyprediction modelsignificant potentialscientific basisrpe scaleoften leadmetrics servingmeans clusteringload taskskey metricsincluding electrocardiogramfindings highlightapproach provides<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 develop a robust fatigue classification and prediction model to assess and forecast firefighters’ fatigue levels. Key metrics, including electrocardiogram (ECG) signals, subjective fatigue ratings, and reaction time data, were utilized. Experiments involving six healthy adult male participants simulated firefighting scenarios, during which subjective fatigue levels (6–20 Borg’s RPE scale) and reaction times were recorded. A five-level fatigue classification was established using K-means clustering, and entropy weight analysis was applied to define a comprehensive fatigue index (F), enabling a three-tier fatigue classification: light, moderate, and severe fatigue. A BP neural network was employed for dynamic fatigue prediction, with 10 features derived from heart rate and heart rate variability (HRV) metrics serving as inputs and the comprehensive fatigue index (F) as the output. The BP neural network model achieved a high prediction accuracy with an R² value of 93.24%, demonstrating its capability to accurately predict firefighters’ fatigue states. This approach provides a scientific basis for optimizing firefighter training protocols and enhancing operational effectiveness during fire rescue missions. The findings highlight the significant potential of this method for advancing firefighter fatigue monitoring and management.</p></div>2025-05-15T18:15:26ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0323911.g003https://figshare.com/articles/figure/K-means_cluster_output_/29081191CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290811912025-05-15T18:15:26Z |
| spellingShingle | K-means cluster output. Mingwei Xu (9548454) 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 |
| status_str | publishedVersion |
| title | K-means cluster output. |
| title_full | K-means cluster output. |
| title_fullStr | K-means cluster output. |
| title_full_unstemmed | K-means cluster output. |
| title_short | K-means cluster output. |
| title_sort | K-means cluster output. |
| topic | 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 |