An Intelligent Evaluation Algorithm for Pilot Flight Training Ability Based on Multimodal Information Fusion.

Overview

1. School of Opto-electronical Engineering, Xi’an Technological University, Xi’an 710021, China
2. School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, China

Abstract

Intelligent-assisted assessment of pilot flight training ability is a method of automating the evaluation of pilots’ flight skills using artificial intelligence. Currently, using AI to assist or replace human instructors in flight skill assessment has become a mainstream research direction in the field of intelligent aviation. Existing flight skill assessment methods suffer from limitations in data types and insufficient assessment accuracy. To address these issues, we evaluate and predict pilot performance in simulated flight missions based on physiological signals. Following the “OODA loop” theory, we established a multimodal dataset including pilot eye movement, electroencephalogram (EEG), electrocardiogram (ECG), electrodermal signaling (EDS), heart rate, respiration, and flight attitude data. This dataset records changes in physiological rhythms and flight behaviors during pilots’ flight training at different difficulty levels. To enhance the signal-to-noise ratio, we propose an enhanced wavelet fuzzy thresholding denoising algorithm utilizing LSTM optimization. We address the problem of isolated features across different time frames in multimodal data modeling by introducing a multi-feature fusion algorithm based on STFT. Furthermore, by combining a high-efficiency sub-attention mechanism with a Transformer network, we construct a multi-classification network for intelligent-assisted assessment of pilot flight training ability, further improving the output accuracy of each category. Experiments show that our designed algorithm can achieve a classification accuracy of up to 85% on the dataset (5-fold cross-validation), which meets the requirements for auxiliary assessment of flight capabilities.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper links to its data, not to its authors' code: see the Data section.

The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

• doi:10.13026/tjpc-fm02 — at the source; found in “Data Availability Statement”
• github.com/617222535qqcom/evaluate-and-test-the-flight-behavior-of-pilots-in-simulated-flight-training — at github.com; found in “Data Availability Statement”
• physionet.org/content/virtual-reality-piloting — at PhysioNet; found in “Data Availability Statement”

Data Availability Statement

The network training dataset and related program code used in this experiment have been uploaded to the github project. The data link is: https://github.com/617222535qqcom/Evaluate-and-test-the-flight-behavior-of-pilots-in-simulated-flight-training. (accessed on 1 May 2025). The DOI of the control dataset for this experiment is: https://doi.org/10.13026/tjpc-fm02. (accessed on 1 May 2025). The network link is: https://physionet.org/content/virtual-reality-piloting/1.0.0/. (accessed on 1 May 2025).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 4 keywords, 11 MeSH terms, 2 funders, 15 references.

Cite

This paper

Zhang, H., Wang, C., & Wang, P. (2026). An Intelligent Evaluation Algorithm for Pilot Flight Training Ability Based on Multimodal Information Fusion. Sensors (Basel, Switzerland), 26(7), 2245. https://doi.org/10.3390/s26072245

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