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cognitive workload Assessment of Aircraft Pilots

Journal of Behavioral and Brain Science 2022年第10期12卷 474-484页

作者： Maxime Antoine Hamdi Ben Abdessalem Claude Frasson Departement d’Informatique et de Recherche Operationnelle Universite de Montreal Montreal Canada

In this research, we study the cognitive workload of aircraft pilots during a simulated takeoff procedure. We propose a proof-of-concept setup environment to gather heart rate, pupil dilation, and brain cognitive workload data during an A320 takeoff within a simulator. Experiments were performed during which we collected 136 takeoffs across 13 pilots for more than 9 hours of time-series data. Moreover, this paper investigates the correlations between heart rate, pupil dilation, and cognitive workload during such exercise and found that a spike in cognitive load during a critical moment, such as an engine failure, augments a pilot’s heart rate and pupil dilation. Results show that a critical moment within a takeoff procedure increases a pilot’s cognitive load. Next, we used a stacked-LSTM model to predict cognitive workload 5 seconds into the future. The model was able to produce accurate predictions.

关键词： cognitive workload Aviation Heart Rate Pupil Dilation Machine Learning Deep Learning EEG

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An ELM-based Deep SDAE Ensemble for Inter-Subject cognitive workload Estimation with Physiological Signals

An ELM-based Deep SDAE Ensemble for Inter-Subject Cognitive ...

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第三十九届中国控制会议

作者： Zhanpeng Zheng Zhong Yin Jianhua Zhang Engineering Research Center of Optical Instrument and System Ministry of EducationShanghai Key Lab of Modern Optical SystemUniversity of Shanghai for Science and Technology

Evaluating operator cognitive workload（CW） levels in human-machine systems based on neurophysiological signals is becoming the basis to prevent serious accidents due to abnormal state of human *** study proposes an inter-subject CW classifier,extreme learning machine（ELM）-based deep stacked denoising autoencoder ensemble（ED-SDAE）,to adapt the variations of the electroencephalogram（EEG） feature distributions across different *** ED-SDAE consists of two cascade-connected modules,which are termed as high level personalized feature abstractions and abstraction *** combination of SDAE and locality preserving projection（LPP） technique is regarded as base learner to obtain ensemble members for training meta-classifier by stacking-based *** ELM model with Q-statistics diversity measurement is acted as meta-classifier to fuse above inputs to improve classification *** feasibility of the SD-SDAE is tested by two EEG *** multi-class classification rate achieves 0.6353 and 0.6747 for T1 and T2 respectively,and significantly outperforms several shallow and deep CW *** computing the main time complexity,the computational workload of the ED-SDAE is also acceptable for high-dimensional EEG features.

关键词： cognitive workload EEG ensemble learning extreme learning machine stacked denoising autoencoder

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