title: Design of an Adaptive Human-Machine System Based on Dynamical Pattern Recognition of Cognitive Task-Load.
abstract: This paper developed a cognitive task-load (CTL) classification algorithm and allocation strategy to sustain the optimal operator CTL levels over time in safety-critical human-machine integrated systems. An adaptive human-machine system is designed based on a non-linear dynamic CTL classifier, which maps a set of electroencephalogram (EEG) and electrocardiogram (ECG) related features to a few CTL classes. The least-squares support vector machine (LSSVM) is used as dynamic pattern classifier. A series of electrophysiological and performance data acquisition experiments were performed on seven volunteer participants under a simulated process control task environment. The participant-specific dynamic LSSVM model is constructed to classify the instantaneous CTL into five classes at each time instant. The initial feature set, comprising 56 EEG and ECG related features, is reduced to a set of 12 salient features (including 11 EEG-related features) by using the locality preserving projection (LPP) technique. An overall correct classification rate of about 80% is achieved for the 5-class CTL classification problem. Then the predicted CTL is used to adaptively allocate the number of process control tasks between operator and computer-based controller. Simulation results showed that the overall performance of the human-machine system can be improved by using the adaptive automation strategy proposed.
authors.affiliation: Intelligent Systems Group, School of Information Science and Engineering, East China University of Science and Technology Shanghai, China.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Zhang
authors.affiliation: School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology Shanghai, China.
authors.forename: Zhong
authors.initials: Z
authors.lastname: Yin
authors.affiliation: Department of Mathematics, Institute of Cognitive Neurodynamics, School of Science, East China University of Science and Technology Shanghai, China.
authors.forename: Rubin
authors.initials: R
authors.lastname: Wang
keywords: adaptive functional allocation; cognitive task-load; dynamic pattern recognition; electrophysiology; man-machine system