An Intelligent Monitoring Method of Pilot's Operating State Based on PCA and WOA-KELM

Abstract: In this paper, a pilot’s operating state monitoring method based on Whale Optimization Algorithm (WOA) and Kernel-based Extreme Learning Machine (KELM), is introduced to improve the monitoring accuracy of pilot’s operating state.In the first place, collect the peripheral physiological signal data via portable and wearable devices to construct a feature set containing 89 features. Secondly, the Principal Component Analysis (PCA) is employed to reduce the feature dimensionality, and iterative optimization is performed to the key parameters in KELM with Whale Optimization Algorithm. Finally, establish the WOA-KELM recognition model based on these optimized parameters to monitor the pilot’s operating state. The method also overcomes the challenges of poor robustness of single physiological signal, insufficient reliability of the selected features according to previous experience, as well as low recognition accuracy of the classification models. By comparison with the performance verification data of the typical recognition model, the proposed method presents a higher recognition accuracy in monitoring pilot’s operating state.The study firstly creates corresponding operating states and collects physiological data by carrying out flight mission experiment. Flight mission simulation platform is used to accomplish the flight test. This platform is composed of curved screen display, touch screen display, control components, simulator host and other hardware components. Depending on Falcon BMS flight simulation software, various actual combating missions by fighters can be simulated with high fidelity. In this study, the multi-modal human factor perception terminal PTES100 from PsychTech for the gathering of GSR and PPG signals is selected. The sampling frequency of the GSR sensor is 4 Hz, and the sampling frequency of the PPG sensor is 100 Hz. The bracelet is worn on the left wrist of the subject, and the data is transmitted to the computing terminal for processing through blue-tooth.14 subjects were recruited for this study, all of whom were practitioners with aviation knowledge background. After operation trainings, they were relatively familiar with the flight driving operation and had certain basis of using Falcon BMS flight simulation software.The study set the number of features after PCA dimensionality reduction to 10, reducing 89 features to 10 dimensions while preserving the original feature information as much as possible. Secondly, the data set was divided at a ratio of training set accounting for 80 percent, verification set for 10 percent and testing set for 10 percent, namely, 650 pieces of training data, 81 pieces of testing data, and 81 pieces of verification data. WOA algorithm was used to optimize the regularization parameter C and kernel parameter γ of KELM. The population quantity of the whale swarm was set to 100 and the number of iterations was set to 30 to find out the optimal model with the prediction accuracy of the verification set as the fitness function.In terms of model performance verification results, the prediction accuracy of this model in the test set is 96.3%, indicating that it has high recognition performance.

Keywords: Principal Component Analysis (PCA), Whale Optimization Algorithm (WOA), Kernel-based Extreme Learning Machine (KELM), personnel operating state monitoring, peripheral physiological signal

DOI: 10.54941/ahfe1005061

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