Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments
Cognitive load is generated by pilots in the process of information cognition about aircraft control, and it is closely related to flight safety. Cognitive load is the physiological and psychological need that a pilot produces when completing a mission. Therefore, it is meaningful to study the dynam...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/5640784 |
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| _version_ | 1849683661681590272 |
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| author | Haibo Wang Naiqi Jiang Ting Pan Haiqing Si Yao Li Wenjing Zou |
| author_facet | Haibo Wang Naiqi Jiang Ting Pan Haiqing Si Yao Li Wenjing Zou |
| author_sort | Haibo Wang |
| collection | DOAJ |
| description | Cognitive load is generated by pilots in the process of information cognition about aircraft control, and it is closely related to flight safety. Cognitive load is the physiological and psychological need that a pilot produces when completing a mission. Therefore, it is meaningful to study the dynamic identification of the cognitive load of the pilot under the complex human-aircraft-environment interaction. In this paper, the airfield traffic pattern flight simulation experiment was designed and used to obtain the ECG physiological and NASA-TLX psychological data. The wavelet transform preprocessing and mathematical statistics analysis were applied on them, respectively. Furthermore, the Pearson correlation analysis method is used to select the characteristic indicators of psycho-physiological data after preprocessing. Based on the psycho-physiological characteristic indicators, the pilot’s cognitive load identification model is constructed by combining RNN and LSTM. The results of this study are more accurate compared with the cognitive load identification models established by other methods such as RNN neural network and support vector machine. This research is able to provide a useful reference for preventing and reduction of human error caused by the cognitive load during flight missions. It will be potential to realize intelligent control of aircraft cockpit, improving the flight control behavior and maintaining flight safety. |
| format | Article |
| id | doaj-art-d484ffd502c746b4a6ac2ffca7ae289e |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-d484ffd502c746b4a6ac2ffca7ae289e2025-08-20T03:23:46ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/56407845640784Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex EnvironmentsHaibo Wang0Naiqi Jiang1Ting Pan2Haiqing Si3Yao Li4Wenjing Zou5School of Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Civil Aviation and Flight, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaCognitive load is generated by pilots in the process of information cognition about aircraft control, and it is closely related to flight safety. Cognitive load is the physiological and psychological need that a pilot produces when completing a mission. Therefore, it is meaningful to study the dynamic identification of the cognitive load of the pilot under the complex human-aircraft-environment interaction. In this paper, the airfield traffic pattern flight simulation experiment was designed and used to obtain the ECG physiological and NASA-TLX psychological data. The wavelet transform preprocessing and mathematical statistics analysis were applied on them, respectively. Furthermore, the Pearson correlation analysis method is used to select the characteristic indicators of psycho-physiological data after preprocessing. Based on the psycho-physiological characteristic indicators, the pilot’s cognitive load identification model is constructed by combining RNN and LSTM. The results of this study are more accurate compared with the cognitive load identification models established by other methods such as RNN neural network and support vector machine. This research is able to provide a useful reference for preventing and reduction of human error caused by the cognitive load during flight missions. It will be potential to realize intelligent control of aircraft cockpit, improving the flight control behavior and maintaining flight safety.http://dx.doi.org/10.1155/2020/5640784 |
| spellingShingle | Haibo Wang Naiqi Jiang Ting Pan Haiqing Si Yao Li Wenjing Zou Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments Journal of Advanced Transportation |
| title | Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments |
| title_full | Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments |
| title_fullStr | Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments |
| title_full_unstemmed | Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments |
| title_short | Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments |
| title_sort | cognitive load identification of pilots based on physiological psychological characteristics in complex environments |
| url | http://dx.doi.org/10.1155/2020/5640784 |
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