Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse Velocity
Fatigue affects operators’ safe operation in a nuclear power plant’s (NPP) main control room (MCR). An accurate and rapid detection of operators’ fatigue status is significant to safe operation. The purpose of the study is to explore a way to detect operator fatigue using trends in eyes’ blink rate,...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/4/2718 |
| _version_ | 1797622524452798464 |
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| author | Licao Dai Yu Li Meihui Zhang |
| author_facet | Licao Dai Yu Li Meihui Zhang |
| author_sort | Licao Dai |
| collection | DOAJ |
| description | Fatigue affects operators’ safe operation in a nuclear power plant’s (NPP) main control room (MCR). An accurate and rapid detection of operators’ fatigue status is significant to safe operation. The purpose of the study is to explore a way to detect operator fatigue using trends in eyes’ blink rate, number of frames closed in a specified time (PERCLOS) and mouse velocity changes of operators. In experimental tasks of simulating operations, the clustering method of Toeplitz Inverse Covariance-Based Clustering (TICC) is used for the relevant data captured by non-invasive techniques to determine fatigue levels. Based on the determined results, the data samples are given labeled fatigue levels. Then, the data of fatigue samples with different levels are identified using supervised learning techniques. Supervised learning is used to classify different fatigue levels of operators. According to the supervised learning algorithm in different time windows (20 s–60 s), different time steps (10 s–50 s) and different feature sets (eye, mouse, eye-plus-mouse) classification performance show that K-Nearest Neighbor (KNN) perform the best in the combination of the above multiple indexes. It has an accuracy rate of 91.83%. The proposed technique can detect operators’ fatigue level in real time within 10 s. |
| first_indexed | 2024-03-11T09:11:31Z |
| format | Article |
| id | doaj.art-ee53eb455fb54ebd929a81444219caff |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T09:11:31Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ee53eb455fb54ebd929a81444219caff2023-11-16T18:59:46ZengMDPI AGApplied Sciences2076-34172023-02-01134271810.3390/app13042718Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse VelocityLicao Dai0Yu Li1Meihui Zhang2Institute of Human Factors, University of South China, Hengyang 421001, ChinaInstitute of Human Factors, University of South China, Hengyang 421001, ChinaInstitute of Human Factors, University of South China, Hengyang 421001, ChinaFatigue affects operators’ safe operation in a nuclear power plant’s (NPP) main control room (MCR). An accurate and rapid detection of operators’ fatigue status is significant to safe operation. The purpose of the study is to explore a way to detect operator fatigue using trends in eyes’ blink rate, number of frames closed in a specified time (PERCLOS) and mouse velocity changes of operators. In experimental tasks of simulating operations, the clustering method of Toeplitz Inverse Covariance-Based Clustering (TICC) is used for the relevant data captured by non-invasive techniques to determine fatigue levels. Based on the determined results, the data samples are given labeled fatigue levels. Then, the data of fatigue samples with different levels are identified using supervised learning techniques. Supervised learning is used to classify different fatigue levels of operators. According to the supervised learning algorithm in different time windows (20 s–60 s), different time steps (10 s–50 s) and different feature sets (eye, mouse, eye-plus-mouse) classification performance show that K-Nearest Neighbor (KNN) perform the best in the combination of the above multiple indexes. It has an accuracy rate of 91.83%. The proposed technique can detect operators’ fatigue level in real time within 10 s.https://www.mdpi.com/2076-3417/13/4/2718fatiguenuclear power plant main control roomeyes’ blink ratePERCLOSmouse velocityclustering |
| spellingShingle | Licao Dai Yu Li Meihui Zhang Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse Velocity Applied Sciences fatigue nuclear power plant main control room eyes’ blink rate PERCLOS mouse velocity clustering |
| title | Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse Velocity |
| title_full | Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse Velocity |
| title_fullStr | Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse Velocity |
| title_full_unstemmed | Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse Velocity |
| title_short | Detection of Operator Fatigue in the Main Control Room of a Nuclear Power Plant Based on Eye Blink Rate, PERCLOS and Mouse Velocity |
| title_sort | detection of operator fatigue in the main control room of a nuclear power plant based on eye blink rate perclos and mouse velocity |
| topic | fatigue nuclear power plant main control room eyes’ blink rate PERCLOS mouse velocity clustering |
| url | https://www.mdpi.com/2076-3417/13/4/2718 |
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