Cognitive Load Identification of Pilots Based on Physiological-Psychological Characteristics in Complex Environments
| Autor: | Yao Li, Ting Pan, Naiqi Jiang, Haiqing Si, Haibo Wang, Wenjing Zou |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Economics and Econometrics
Article Subject Computer science Strategy and Management Human error Machine learning computer.software_genre Flight simulator 050105 experimental psychology 0501 psychology and cognitive sciences 050107 human factors HE1-9990 TA1001-1280 Artificial neural network business.industry Mechanical Engineering 05 social sciences Cognition Airfield traffic pattern Computer Science Applications Cockpit Transportation engineering Automotive Engineering Artificial intelligence business Intelligent control computer Transportation and communications Cognitive load |
| Zdroj: | Journal of Advanced Transportation, Vol 2020 (2020) |
| ISSN: | 0197-6729 |
| DOI: | 10.1155/2020/5640784 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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