Abstract
This study aimed to evaluate workload by detecting Heart Rate Variability (HRV) indexes in a sample of 34 pilots (with a mean age of 33 years) while performing simulated flight exercises. A one-way ANOVA with repeated measures was performed to assess the changes of the physiological measures in five standard maneuvers associated with different workload levels. The results show that all the indexes, but the Low Frequency to High Frequency ratio index (LF/HF), have a well-defined trend between the baseline and the en-route phase and with the three phases takeoff, steady turn, and landing. This study, as main findings, provides evidence of a differentiation among low, medium, and high workload levels using the time, frequencies, and non-linear HRV domains of analysis. These findings support the relevance of HRV indexes for workload evaluation, suggesting the development of non-invasive instruments capable of assessing workload in real-time. Further studies may be conducted to investigate whether the same findings could also be applied to more challenging maneuvers in real working conditions.
Highlights
T HE handling qualities of the aircraft are related to the maneuvers workload level experienced by the pilot while performing a specific task during the different flight phases [1]
Fuentes-García et al [8] examined pilots performing a real flight with an F5 aircraft and a simulated flight with an operational F-5M flight simulator by analyzing the variations in objective parameters related to heart rate variability
Regarding the frequencydomain Heart Rate Variability (HRV) indexes, females compared to males reported lower scores on VLF, LF, Low Frequency to High Frequency ratio index (LF/HF) ratio, the total power (T OTpow), and higher HF [64]
Summary
T HE handling qualities of the aircraft are related to the maneuvers workload level experienced by the pilot while performing a specific task during the different flight phases [1]. Mohanavelu et al [7] analyzed missions carried out on a simulator with different workload levels in terms of performance and additional tasks. Klyde et al [9] ran tests with a flight simulator and used electroencephalogram (EEG) and electrocardiogram (ECG), as physiologically based techniques, to estimate handling qualities. Their results demonstrated the feasibility of artificial neural networks in using different physiological measurements
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