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

• Published in: IEEE transactions on human-machine systems Vol. 45; no. 2; pp. 200 - 214
• Main Authors: Zhang, Jianhua, Yin, Zhong, Wang, Rubin
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Classification; Computer simulation; Data analysis; Electrocardiography; Electroencephalography; Electrooculography; Human; machine learning; man-machine system; mental workload (MWL); Operators; psychophysiology; Reduction; Smoothing methods; Support vector machines; Workload; mental workload (MWL); psychophysiology; man–machine system; machine learning; Classification
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2014.2366914

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.