Identification of Pilots’ Fatigue Status Based on Electrocardiogram Signals

Abstract

Fatigue is an important factor affecting modern flight safety. It can easily lead to a decline in pilots’ operational ability, misjudgments, and flight illusions. Moreover, it can even trigger serious flight accidents. In this paper, a wearable wireless physiological device was used to obtain pilots’ electrocardiogram (ECG) data in a simulated flight experiment, and 1440 effective samples were determined. The Friedman test was adopted to select the characteristic indexes that reflect the fatigue state of the pilot from the time domain, frequency domain, and non-linear characteristics of the effective samples. Furthermore, the variation rules of the characteristic indexes were analyzed. Principal component analysis (PCA) was utilized to extract the features of the selected feature indexes, and the feature parameter set representing the fatigue state of the pilot was established. For the study on pilots’ fatigue state identification, the feature parameter set was used as the input of the learning vector quantization (LVQ) algorithm to train the pilots’ fatigue state identification model. Results show that the recognition accuracy of the LVQ model reached 81.94%, which is 12.84% and 9.02% higher than that of traditional back propagation neural network (BPNN) and support vector machine (SVM) model, respectively. The identification model based on the LVQ established in this paper is suitable for identifying pilots’ fatigue states. This is of great practical significance to reduce flight accidents caused by pilot fatigue, thus providing a theoretical foundation for pilot fatigue risk management and the development of intelligent aircraft autopilot systems.

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks

“…Karthikeyan et al employed a 1-h visuo-spatial 2-back task to study the effects of anodal transcranial direct current stimulation on working memory under fatigue ( Karthikeyan et al, 2021 ). Additionally, a 60 min Stoop color-word task ( Nikooharf Salehi et al, 2022 ) and a flight simulator task ( Pan et al, 2021 ) were used to induce a mental state of fatigue. While these models may be suitable for scenarios involving static operators, they might not be applicable to actual dynamic settings, such as those encountered by pilots, firefighters and first responders.…”

“…Studies have demonstrated a correlation between ECG signals and psychomotor vigilance task (PVT) performance, as well as the effectiveness of HRV indices in assessing cognitive task-related errors. Leveraging sensitive features extracted from ECG, algorithms like learning vector quantization and random forest tree classifiers achieve impressive accuracy in identifying fatigue states, underscoring HRV as a potential indicator for evaluating worker fatigue ( Chua et al, 2012 ; Zhao et al, 2012 ; Pan et al, 2021 ; Xu et al, 2021 ; Takada et al, 2022 ).…”

A Comprehensive Analysis of Machine Learning and Deep Learning Models for Identifying Pilots’ Mental States from Imbalanced Physiological Data

“…HE ongoing evolution of the aviation industry hinges on maintaining rigorous safety standards, as advancements in aircraft design, endurance, and safety have contributed to a worldwide decrease in aircraft accidents [1,2]. Cognitive tendencies, particularly those related to attentional focus, are common among pilots and can be elicited by various factors such as cockpit alerts, extreme weather turbulence, takeoff, and landing.…”

Beam Offset Detection in Laser Stake Welding of Tee Joints Using Machine Learning and Spectrometer Measurements

“…Machine learning techniques are widely used for classification and prediction problems in diverse fields [ 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 ] as well as welding monitoring and discontinuity detection. In a research conducted by Chen et al [ 33 ] artificial neural network (ANN) and support vector machine (SVM) were employed to classify welding defects using emission spectrum data.…”