Nonparametric Hierarchical Hidden Semi-Markov Model for Brain Fatigue Behavior Detection of Pilots During Flight
The evaluation of pilot brain activity is very important for flight safety. This study proposes a Hidden semi-Markov Model with Hierarchical prior to detect brain activity under different flight tasks. A dynamic student mixture model is proposed to detect the outlier of emission probability of HSMM. Instantaneous spectrum features are also extracted from EEG signals. Compared with other latent variable models, the proposed model shows excellent performance for the automatic inference of brain cognitive activity of pilots. The results indicate that the consideration of hierarchical model and the emission probability with <inline-formula> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> mixture model improves the recognition performance for Pilots’ fatigue cognitive level.
- Record URL:
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
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Supplemental Notes:
- Copyright © 2022, IEEE.
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Authors:
- Wu, Edmond Q
- Zhu, Li-Min
- Li, Gui-Jiang
- Li, Hong-Jun
- Tang, Zhiri
- Hu, Ruihan
- Zhou, Gui-Rong
- Publication Date: 2022-6
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 5245-5256
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Serial:
- IEEE Transactions on Intelligent Transportation Systems
- Volume: 23
- Issue Number: 6
- Publisher: Institute of Electrical and Electronics Engineers (IEEE)
- ISSN: 1524-9050
- Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979
Subject/Index Terms
- TRT Terms: Air pilots; Aviation safety; Cognition; Electroencephalography; Fatigue (Physiological condition); Markov processes; Monitoring
- Subject Areas: Aviation; Safety and Human Factors;
Filing Info
- Accession Number: 01852076
- Record Type: Publication
- Files: TRIS
- Created Date: Jul 21 2022 11:30AM