An Intellectual–Analytical Platform for Assessing the Psychophysiological Load on Flight Instructors
This study aimed to develop an intellectual and analytical platform for assessing the psychophysiological load on flight instructors in a flight school (general aviation). As part of this study, an information model for evaluating the working environment’s load based on noise levels was developed, a...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/5917 |
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| Summary: | This study aimed to develop an intellectual and analytical platform for assessing the psychophysiological load on flight instructors in a flight school (general aviation). As part of this study, an information model for evaluating the working environment’s load based on noise levels was developed, a model to predict individual psychophysiological load was created, an expert model to assess mental health was established, and a hybrid model was devised to determine the overall psychophysiological load on an instructor while performing their duties. Noise load was measured during flights with two aircraft (Zlín Z43 and Diamond DA-40 TDI), resulting in the acquisition of 4,361,300 data points. This dataset was collected during two data acquisition sessions for each aircraft, encompassing three phases of flight: takeoff, in-flight, and landing. During the flight, noise measurements were conducted based on five indicators: sound pressure, fluctuation strength, roughness, sharpness, and tonality. Based on the measured data, the platform was verified and configured, and example evaluations were demonstrated. This study employed modern methods of intelligent data analysis, utilizing both univariate and multivariate membership functions. The developed platform incorporates quantitative dynamic data obtained from devices measuring psychophysiological load, integrating professional mental health assessments and predicting dynamic work environment indicators for modeling load trends. Early detection of critical load levels helps protect the health of flight instructors, thus creating a safe working environment for training new pilots. |
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| ISSN: | 2076-3417 |