Xu, L.; Ma, S.; Shen, Z.; Huang, S.; Nan, Y. Analyzing Multi-Mode Fatigue Information from Speech and Gaze Data from Air Traffic Controllers. Aerospace2024, 11, 15.
Xu, L.; Ma, S.; Shen, Z.; Huang, S.; Nan, Y. Analyzing Multi-Mode Fatigue Information from Speech and Gaze Data from Air Traffic Controllers. Aerospace 2024, 11, 15.
Xu, L.; Ma, S.; Shen, Z.; Huang, S.; Nan, Y. Analyzing Multi-Mode Fatigue Information from Speech and Gaze Data from Air Traffic Controllers. Aerospace2024, 11, 15.
Xu, L.; Ma, S.; Shen, Z.; Huang, S.; Nan, Y. Analyzing Multi-Mode Fatigue Information from Speech and Gaze Data from Air Traffic Controllers. Aerospace 2024, 11, 15.
Abstract
An algorithm is proposed for discriminating the fatigue state of air traffic controllers based on ap-plying multispeech feature fusion using an FSVM to voice data, and for extracting eye-fatigue-state discrimination features based on PERCLOS eye data. For the speech algorithm and an eye-fatigue index, a new controller fatigue-state evaluation index based on the entropy weight method is proposed based on decision-level fusion of fatigue discrimination results for speech and the eyes. Experimental results show that the fatigue-state recognition accuracy rate was 84.81% for the fatigue state evaluation index, which was 3.36% and 1.86% higher than those for speech and eye assessments, respectively. The comprehensive fatigue evaluation index provides important reference values for controller scheduling and mental-state evaluations.
Keywords
fatigue recognition; Air traffic controller; Feature fusion; Multi-mode; scheduling
Subject
Engineering, Aerospace Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
