文献类型：期刊文献

英文题名：Recognition of Mental Workload Levels Under Complex Human-Machine Collaboration by Using Physiological Features and Adaptive Support Vector Machines

作者：Zhang, Jianhua[1];Yin, Zhong[1];Wang, Rubin[2]

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

年份：2015

卷号：45

期号：2

起止页码：200

外文期刊名：IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS

收录：;EI(收录号：20151200664352);WOS:【SCI-EXPANDED(收录号:WOS:000351468500005)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61075070 and Key Grant 11232005. This paper was recommended by Associate Editor D. B. Kaber.

语种：英文

外文关键词：Classification; machine learning; man-machine system; mental workload (MWL); psychophysiology

摘要：In order to detect human operator performance degradation or breakdown, this paper proposes an adaptive support vector machine-based method to classify operator mental workload (MWL) into few discrete levels based on psychophysiological measures. Electroencephalogram, electrocardiogram, and electrooculography signals were recorded continuously while the operator was performing safety-critical process control operations in a simulated human-machine system. In coarse-grained analysis, the adaptive exponential smoothing (AES) technique is used to smooth the psychophysiological data and to remove strong artifacts without requiring templates. The MWL level is classified every 30 s by using bounded support vector machine (BSVM) and tenfold cross-validation techniques. Locality preservation projection (LPP) technique is utilized to derive salient psychophysiological features by means of feature reduction. By combining the AES-LPP and BSVM methods, the accuracy of the coarse-grained MWL classification was significantly improved by 11-13%. On the other hand, to perform MWL classification with higher temporal resolution and cross-subject and cross-trial generalizability, finer-grained data analysis is also conducted to recognize MWL levels every 5 s based on a combination of adaptive BSVM (ABSVM) and AES techniques. In comparison with the use of the BSVM algorithm alone, a significant performance improvement by 10-20% is achieved by using the AES-ABSVM method in the finer-grained MWL classification.