Prediction Method of Human Fatigue in an Artificial Atmospheric Environment Based on Dynamic Bayesian Network

Abstract

Fatigue state usually leads to slow reaction of the human body and its thoughts. It is an important factor causing significant decline in the working ability of workers, an increase in error rate and even major accidents. It would have a more negative impact in an artificial atmospheric environment. The effective prediction of fatigue can contribute to improved working efficiency and reduce the occurrence of accidents. In this paper, a prediction method of human fatigue in an artificial atmospheric environment was established, combining as many as eight input parameters about the cause and effect of human fatigue based on a dynamic Bayesian network in order to achieve a relatively comprehensive and accurate prediction of human fatigue. This fatigue prediction method was checked by experimental results. The results indicate that the established prediction method could provide a relatively reliable way to predict a worker fatigue state in an artificial atmospheric working environment.