A human workload monitoring method considering qualitative and quantitative data fusion

Abstract

Nowadays, human workload plays an important role in human machine system's reliability and safety area. No matter in driving, flying or other field, excessive workload may lead to the ignorance of key message, which is of importance in mission accomplishment. Traditional research method uses only these quantitative physiological parameters (such as EEG,EMG or eye movement parameters) to monitor the workload state. Although physiological parameters alone have certain relation with workload degree, it usually cannot reflect the state comprehensively and effectively. In other words, the relation between single parameter and workload is not sufficient. Besides, qualitative parameter like human mood and workload before experiment is another important input parameter that should take account of. In order to deal with the qualitative and quantitative input in the meantime, fuzzy neural network is adopted as a tool to monitor the workload state. At last, experiment's data is utilized to illustrate the effectiveness of above-mentioned method compared with either physiological parameter or eyes movement parameter.