Using hidden semi-Markov model with hierarchical Dirichlet process to infer pilots' fatigue states

被引：0
作者：
Luo Y.-X. [1 ]
Jia B. [2 ]
Qiu X.-Y. [3 ]
Deng P.-Y. [3 ]
Wu Q. [1 ]
机构：
[1] School of Electronic Information and Electrical Engineering, Shanghai JiaoTong University, Shanghai
[2] China Eastern Airlines Technology Application R&D Center Co., Ltd, Shanghai
[3] China National Aeronautical Radio Electronics Research Institute, Shanghai
来源：
Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2020年 / 37卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Electroencephalogram signals; Hidden semi-Markov model; Pilots' fatigue; Smooth pseudo-affine Wigner-Ville distribution;
D O I：
10.7641/CTA.2019.90311
中图分类号：
学科分类号：
摘要：
In the civil aviation and military aviation field, the long flight and harsh conditions will make pilots feel fatigued. The pilots' fatigue will seriously affect their judgment, so the inference of fatigue state is important to ensure flight safety. Brain fatigue cognition faces two main problems: one is how to extract feature of fatigue cognition, and the other is how to identify the latent state of brain cognition with duration. For the first problem, a method based on smooth pseudo-affine Wigner-Ville distribution (SPAWVD) with kaiser window function was proposed to calculate the instantaneous spectral features of Electroencephalogram (EEG) rhythms. Features extracted by this method had better local significance. For the second problem, a residual life hidden semi-Markov model (HSMM) was established to learn the dynamic mechanism of the brain. It modeled the duration of brain fatigue cognitive states and avoids fast switching between states caused by hidden semi-Markov model (HMM). Then a multi-layer learning network based on hierarchical Dirichlet process (HDP) was built to provide subtasks that share the subject of fatigue awareness. The result of the experiment was satisfactory and proved that the model had a high ability to identify pilots' latent brain cognitive state. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.
引用
收藏
页码：1196 / 1206
页数：10
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