The mapping relationship between PERCLOS and work fatigue: a correlation verification experiment based on radial basis function neural network

Abstract

Work fatigue is one of the main causes of accidents. The main purpose of this article is to discuss the mapping relationship between eyelid closure data and human fatigue at work through confirmatory experiments, and the applicability of composite eye movement parameter analysis based on the random forest neural network model for fatigue detection in specific tasks . A total of 16 subjects were recruited in this experiment. The performance of the subjects was obtained by the improved measurement method of the number of cancellation symbols, and the reaction time of the subjects was obtained by the two-point click reaction time measurement method. The obtained performance and response time data are used to reflect the fatigue degree of the subjects, and the Diskablis eye tracker is used to record the eye movement parameters of the subjects. Finally, it was found that PERCLOS and the two-point click response time had a correlation with fatigue status in time sequence, and there was a more potential relationship between other performance parameters and fatigue. The comprehensive eye movement parameter analysis based on the random forest neural network model has also been confirmed to have high usability in fatigue detection.

Keywords: Perclos, Fatigue, Neural Network

DOI: 10.54941/ahfe1001082