A total of 83 selected features.
<div><p>Control room operators encounter a substantial risk of mental fatigue, which can reduce their human reliability by diminishing concentration and responsiveness, leading to unsafe operations. There is value in detection of individuals’ mental fatigue status in the workplace. This...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2025
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1852021421613514752 |
|---|---|
| author | Zuzhen Ji (17593348) |
| author2 | Xian Xie (9622804) Enjing Jiang (21029913) Yuchen Wang (2036299) Bohan Min (21029916) Shuanghua Yang (16940404) Yong Chen (109188) Dirk Pons (21029919) |
| author2_role | author author author author author author author |
| author_facet | Zuzhen Ji (17593348) Xian Xie (9622804) Enjing Jiang (21029913) Yuchen Wang (2036299) Bohan Min (21029916) Shuanghua Yang (16940404) Yong Chen (109188) Dirk Pons (21029919) |
| author_role | author |
| dc.creator.none.fl_str_mv | Zuzhen Ji (17593348) Xian Xie (9622804) Enjing Jiang (21029913) Yuchen Wang (2036299) Bohan Min (21029916) Shuanghua Yang (16940404) Yong Chen (109188) Dirk Pons (21029919) |
| dc.date.none.fl_str_mv | 2025-04-09T17:33:48Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320780.g008 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/figure/A_total_of_83_selected_features_/28763217 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biotechnology Cancer Mental Health Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified individuals &# 8217 g ., electrocardiograms deep residual network gradient boosting machine assess fatigue states process system operations mfd typically rely collect facial data mental fatigue status machine learning model combines computer vision existing mfd methods mental fatigue detection mental fatigue machine learning computer vision fatigue analysis unsafe operations traditional methods dimensional data biological data substantial risk study introduces results show require operators real situation random forest proposed method new method human reliability diminishing concentration constant contact |
| dc.title.none.fl_str_mv | A total of 83 selected features. |
| dc.type.none.fl_str_mv | Image Figure info:eu-repo/semantics/publishedVersion image |
| description | <div><p>Control room operators encounter a substantial risk of mental fatigue, which can reduce their human reliability by diminishing concentration and responsiveness, leading to unsafe operations. There is value in detection of individuals’ mental fatigue status in the workplace. This study introduces a new method for mental fatigue detection (MFD) that combines computer vision and machine learning. Traditional methods for MFD typically rely on multi-dimensional data for fatigue analysis and detection, which can be challenging to apply in a real situation. The traditional methods such as the use of biological data, e.g., electrocardiograms, require operators to be in constant contact with sensors, while this study utilizes computer vision to collect facial data, and a machine learning model to assess fatigue states. The developed machine learning method consists both Deep Residual Network and Random Forest (DRN-RF). A comparison with existing MFD methods, including K Nearest Neighbors and Gradient Boosting Machine, has been carried out. The results show that the accuracy of the DRN-RF model reaches 94.2% and the deviation is 0.004. Evidently, the DRN-RF model demonstrates high accuracy and stability. Overall, the proposed method has the potential to contribute to improving the safety of process system operations, particularly in the aspect of human factor management.</p></div> |
| eu_rights_str_mv | openAccess |
| id | Manara_0d42fb7679d84fec22d173a4c2bdf97d |
| identifier_str_mv | 10.1371/journal.pone.0320780.g008 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28763217 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A total of 83 selected features.Zuzhen Ji (17593348)Xian Xie (9622804)Enjing Jiang (21029913)Yuchen Wang (2036299)Bohan Min (21029916)Shuanghua Yang (16940404)Yong Chen (109188)Dirk Pons (21029919)BiotechnologyCancerMental HealthSpace ScienceBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedindividuals &# 8217g ., electrocardiogramsdeep residual networkgradient boosting machineassess fatigue statesprocess system operationsmfd typically relycollect facial datamental fatigue statusmachine learning modelcombines computer visionexisting mfd methodsmental fatigue detectionmental fatiguemachine learningcomputer visionfatigue analysisunsafe operationstraditional methodsdimensional databiological datasubstantial riskstudy introducesresults showrequire operatorsreal situationrandom forestproposed methodnew methodhuman reliabilitydiminishing concentrationconstant contact<div><p>Control room operators encounter a substantial risk of mental fatigue, which can reduce their human reliability by diminishing concentration and responsiveness, leading to unsafe operations. There is value in detection of individuals’ mental fatigue status in the workplace. This study introduces a new method for mental fatigue detection (MFD) that combines computer vision and machine learning. Traditional methods for MFD typically rely on multi-dimensional data for fatigue analysis and detection, which can be challenging to apply in a real situation. The traditional methods such as the use of biological data, e.g., electrocardiograms, require operators to be in constant contact with sensors, while this study utilizes computer vision to collect facial data, and a machine learning model to assess fatigue states. The developed machine learning method consists both Deep Residual Network and Random Forest (DRN-RF). A comparison with existing MFD methods, including K Nearest Neighbors and Gradient Boosting Machine, has been carried out. The results show that the accuracy of the DRN-RF model reaches 94.2% and the deviation is 0.004. Evidently, the DRN-RF model demonstrates high accuracy and stability. Overall, the proposed method has the potential to contribute to improving the safety of process system operations, particularly in the aspect of human factor management.</p></div>2025-04-09T17:33:48ZImageFigureinfo:eu-repo/semantics/publishedVersionimage10.1371/journal.pone.0320780.g008https://figshare.com/articles/figure/A_total_of_83_selected_features_/28763217CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/287632172025-04-09T17:33:48Z |
| spellingShingle | A total of 83 selected features. Zuzhen Ji (17593348) Biotechnology Cancer Mental Health Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified individuals &# 8217 g ., electrocardiograms deep residual network gradient boosting machine assess fatigue states process system operations mfd typically rely collect facial data mental fatigue status machine learning model combines computer vision existing mfd methods mental fatigue detection mental fatigue machine learning computer vision fatigue analysis unsafe operations traditional methods dimensional data biological data substantial risk study introduces results show require operators real situation random forest proposed method new method human reliability diminishing concentration constant contact |
| status_str | publishedVersion |
| title | A total of 83 selected features. |
| title_full | A total of 83 selected features. |
| title_fullStr | A total of 83 selected features. |
| title_full_unstemmed | A total of 83 selected features. |
| title_short | A total of 83 selected features. |
| title_sort | A total of 83 selected features. |
| topic | Biotechnology Cancer Mental Health Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified individuals &# 8217 g ., electrocardiograms deep residual network gradient boosting machine assess fatigue states process system operations mfd typically rely collect facial data mental fatigue status machine learning model combines computer vision existing mfd methods mental fatigue detection mental fatigue machine learning computer vision fatigue analysis unsafe operations traditional methods dimensional data biological data substantial risk study introduces results show require operators real situation random forest proposed method new method human reliability diminishing concentration constant contact |