A High-Precision Fatigue Detecting Method for Air Traffic Controllers Based on Revised Fractal Dimension Feature

Abstract

As air traffic volume increases, the air traffic controller (ATC) fatigue has become a major cause for air traffic accidents. However, the conventional fatigue-detecting methods based on speech are neither effective nor accurate because the speech signals are nonlinear and complicated. In this paper, an ATC fatigue-detecting method based on fractal dimension (FD) is proposed. Firstly, a special speech database of ATC radiotelephony communications is constructed. These radiotelephony communications are obtained from Air Traffic Management Shandong Bureau of China. Then, speech signals implement a wavelet decomposition and FD calculation. The calculation result shows the significant difference among the FD of the speech signal before and after fatigue. Furthermore, a novel fatigue feature of the ATC based on the FD of speech is built. A series of experiments are conducted to detect the ATC fatigue with the fatigue feature comparison process and a support vector machine (SVM). The results show that the accuracy in detecting ATC fatigue based on FD was 92.82%, which are higher than the state-of-the art methods. The research provides a theoretical guidance for Air Traffic Management Authority on detecting ATC’s fatigue, while it may provide reference for the fatigue assessment in other professional fields of civil aviation.