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 electroo...