Mental Fatigue Prediction Model Based on Multimodal Fusion
Mental fatigue is a kind of mental exhaustion phenomenon caused by heavy mental work, excessive nervous tension or monotonous work for a long time, which brings great harm to traffic, construction and other fields. Hence, more and more attention has been paid to the research of fatigue prediction me...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8834786/ |
| _version_ | 1818922135358275584 |
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| author | Anping Song Chaoqun Niu Xuehai Ding Xiaokang Xu Ziheng Song |
| author_facet | Anping Song Chaoqun Niu Xuehai Ding Xiaokang Xu Ziheng Song |
| author_sort | Anping Song |
| collection | DOAJ |
| description | Mental fatigue is a kind of mental exhaustion phenomenon caused by heavy mental work, excessive nervous tension or monotonous work for a long time, which brings great harm to traffic, construction and other fields. Hence, more and more attention has been paid to the research of fatigue prediction methods in recent years. The commonly used fatigue prediction methods fail to accurately predict the fatigue state without adding the influence of physiological factors on fatigue. In this paper, we introduce a fatigue prediction model based on subjective alertness model and physiological parameters, which can be used to predict the fatigue state of human body through deep sleep time, sleep time and workload (physiological parameters) in the future. Specifically, the input is the physiological parameters of the previous day, and the output is fatigue state of the following day. In order to avoid individual differences, this model is modified and verified by multimodal real-time monitoring and self-learning methods. Experiments show that our model is effective, accurate and convenient in practice, which is beneficial to fatigue prediction. |
| first_indexed | 2024-12-20T01:48:44Z |
| format | Article |
| id | doaj.art-4469160ce72346beb91504b98ade84d5 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:48:44Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4469160ce72346beb91504b98ade84d52022-12-21T19:57:41ZengIEEEIEEE Access2169-35362019-01-01717705617706210.1109/ACCESS.2019.29410438834786Mental Fatigue Prediction Model Based on Multimodal FusionAnping Song0Chaoqun Niu1https://orcid.org/0000-0003-1765-3991Xuehai Ding2Xiaokang Xu3Ziheng Song4Department of Computer Science and Technology, School of Computer Engineering and Science, Shanghai University, Shanghai, ChinaDepartment of Computer Science and Technology, School of Computer Engineering and Science, Shanghai University, Shanghai, ChinaDepartment of Computer Science and Technology, School of Computer Engineering and Science, Shanghai University, Shanghai, ChinaDepartment of Computer Science and Technology, School of Computer Engineering and Science, Shanghai University, Shanghai, ChinaCollege of Science, Purdue University, West Lafayette, IN, USAMental fatigue is a kind of mental exhaustion phenomenon caused by heavy mental work, excessive nervous tension or monotonous work for a long time, which brings great harm to traffic, construction and other fields. Hence, more and more attention has been paid to the research of fatigue prediction methods in recent years. The commonly used fatigue prediction methods fail to accurately predict the fatigue state without adding the influence of physiological factors on fatigue. In this paper, we introduce a fatigue prediction model based on subjective alertness model and physiological parameters, which can be used to predict the fatigue state of human body through deep sleep time, sleep time and workload (physiological parameters) in the future. Specifically, the input is the physiological parameters of the previous day, and the output is fatigue state of the following day. In order to avoid individual differences, this model is modified and verified by multimodal real-time monitoring and self-learning methods. Experiments show that our model is effective, accurate and convenient in practice, which is beneficial to fatigue prediction.https://ieeexplore.ieee.org/document/8834786/Mental fatigueprediction modelphysiological parameterssubjective alertnessmultimodal |
| spellingShingle | Anping Song Chaoqun Niu Xuehai Ding Xiaokang Xu Ziheng Song Mental Fatigue Prediction Model Based on Multimodal Fusion IEEE Access Mental fatigue prediction model physiological parameters subjective alertness multimodal |
| title | Mental Fatigue Prediction Model Based on Multimodal Fusion |
| title_full | Mental Fatigue Prediction Model Based on Multimodal Fusion |
| title_fullStr | Mental Fatigue Prediction Model Based on Multimodal Fusion |
| title_full_unstemmed | Mental Fatigue Prediction Model Based on Multimodal Fusion |
| title_short | Mental Fatigue Prediction Model Based on Multimodal Fusion |
| title_sort | mental fatigue prediction model based on multimodal fusion |
| topic | Mental fatigue prediction model physiological parameters subjective alertness multimodal |
| url | https://ieeexplore.ieee.org/document/8834786/ |
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