文献类型：期刊文献

英文题名：Classifying human operator functional state based on electrophysiological and performance measures and fuzzy clustering method

作者：Zhang, Jian-Hua[1,2,3];Peng, Xiao-Di[1,3];Liu, Hua[1,3];Raisch, Joerg[3,4];Wang, Ru-Bin[2,3]

机构：[1]E China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China;[2]E China Univ Sci & Technol, Inst Cognit Neurodynam, Shanghai 200237, Peoples R China;[3]Tech Univ Berlin, Control Syst Grp, D-10587 Berlin, Germany;[4]Max Planck Inst Dynam Complex Tech Syst, Syst & Control Theory Grp, D-39106 Magdeburg, Germany

年份：2013

卷号：7

期号：6

起止页码：477

外文期刊名：COGNITIVE NEURODYNAMICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000326735200003)】；

基金：This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61075070 and Key Project No. 11232005. The authors would also like to thank Prof D. Manzey, Technical University Berlin, Germany, for providing the AUTO-CAMS software used in our OFS data acquisition experiments. The constructive comments from the anonymous reviewer are also gratefully acknowledged.

语种：英文

外文关键词：Operator functional state; Fuzzy c-means algorithm; Psychophysiological measures; Feature extraction; Pattern classification

摘要：The human operator's ability to perform their tasks can fluctuate over time. Because the cognitive demands of the task can also vary it is possible that the capabilities of the operator are not sufficient to satisfy the job demands. This can lead to serious errors when the operator is overwhelmed by the task demands. Psychophysiological measures, such as heart rate and brain activity, can be used to monitor operator cognitive workload. In this paper, the most influential psychophysiological measures are extracted to characterize Operator Functional State (OFS) in automated tasks under a complex form of human-automation interaction. The fuzzy c-mean (FCM) algorithm is used and tested for its OFS classification performance. The results obtained have shown the feasibility and effectiveness of the FCM algorithm as well as the utility of the selected input features for OFS classification. Besides being able to cope with nonlinearity and fuzzy uncertainty in the psychophysiological data it can provide information about the relative importance of the input features as well as the confidence estimate of the classification results. The OFS pattern classification method developed can be incorporated into an adaptive aiding system in order to enhance the overall performance of a large class of safety-critical human-machine cooperative systems.