Fatigue condition monitoring method and device

Abstract

The invention provides a fatigue condition monitoring method and device, wherein the method comprises the following steps of building a similar decision making rule used for judging the fatigue condition according to brain wave feature data by using the brain wave feature data as condition attributes in advance, using fatigue judging parameters as decision making attributes and using a variable-precision rough set model; then, collecting the brain waves of a user in real time; next, calculating the attribute values of the brain wave feature data corresponding to the brain waves; finally, determining the fatigue condition of the user according to the attribute value of the brain wave feature data and the similar decision making rule; and completing the real-time monitoring on the fatigue condition of the user. The method and the device provided by the invention have the advantages that the method is simple; the realization is easy; the cost is low; and meanwhile, the accuracy and the real-time performance are high.

Description

technical field [0001] The invention relates to the technical field of fatigue monitoring, in particular to a fatigue state monitoring method and device. Background technique [0002] With the increasing air traffic flow, the workload of air traffic controllers is increasing, and their fatigue level has an important impact on the safety level of air traffic system. ICAO has developed Doc9966 manual of rules and regulations for fatigue risk management. Developed countries in Europe and the United States have successively extended fatigue detection systems or methods for pilots to controller fatigue detection applications. The Civil Aviation Administration of China is guided by ICAO Doc9966, and also clarified the rules of fatigue risk management in the CCAR-121 document. [0003] So far, researchers at home and abroad have proposed a variety of fatigue detection and management methods and systems. The first method is to collect questionnaires from a large number of subject...

AI Technical Summary

Problems solved by technology

In this way, the current physical condition of the controller is directly ignored, which may have a certain impact on the detection results; the third method: most of the currently existing methods suitable for real-time fatigue detection use the method of collecting and recognizing facial features. The method requires high-precision video detection equipment to shoot controllers at any time, and it has no advantages in terms of cost analysis

[0004] Insufficiency of the first method: subject to the subjective influence of the researcher, making the judgment and prediction of the fatigue of the subject inaccurate; shortcoming of the second method: real-time detection cannot be carried out, and a certain period of time can be speculated based on the performance of the subject for a period of time. Whether the controller is fatigued for a period of time, ignoring the current physical condition of the controller, making the fatigue determination and prediction inaccurate; the deficiency of the third method: Although the fatigue status of the controller has been monitored in real time, the realization of this method requires High-precision video detection equipment, high cost, not applicable

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