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.
Highlights
In safety-critical human-machine integrated systems, human operator, and machine are integrated collaboratively to accomplish complex tasks, in which the operator has to adapt to unforeseen disturbances or even system failures under dynamic process task environment
The results demonstrated that the dynamic LSSVM2 model is more accurate than LSSVM1.It should be noted that the choice of adaptive learning methods may affect the cognitive task-load (CTL) predictive accuracy
Compared with static classifiers such as ANN and ANFIS, the dynamic least-squares support vector machine (LSSVM) model takes into account the temporal correlation between current CTL state and past ones as well as current and previous physiological features
Summary
In safety-critical human-machine integrated systems, human operator, and machine are integrated collaboratively to accomplish complex tasks, in which the operator has to adapt to unforeseen disturbances or even system failures under dynamic process task environment. In such fields as public transportation (Yang et al, 2009; Khushaba et al, 2011) aeronautics and astronautics (Sauvet et al, 2014) and nuclear engineering (Bobko et al, 1998), catastrophic accidents may occur due to operator performance breakdown. The terms of CTL in this context and MWL assessed under cognitive tasks are somehow interchangeable, but the former is more suited to describe operator mental stress under complex human-machine cooperative task requirement in the framework of OFS analysis (Byrne and Parasuraman, 1996; Parasuraman and Riley, 1997; Borghini et al, 2014; Lupu et al, 2014)
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