Recognition of Mental Workload Levels by Combining Adaptive Exponential Feature Smoothing and Locality Preservation Projection Techniques
Assessing mental workload(MWL)in real time is crucial for preventing the accidents caused by cognitive overload or inattention of human operators in safety-critical human-machine(HM)systems.Since continuous-time psychophysiological signals reflect the mental stress of humans,classifiers designed by using those signals can be utilized to assess MWL effectively.However,the noise contained in the extracted temporal psychophysiological features may lead to the overlapping of classifications of different MWL levels at each time instant.In this paper,we tackle this problem by combining adaptive exponential smoothing(AES)of those high dimensional physiological features and locality preservation projection techniques(LPP)to improve the MWL classification performance.In a simulated HM-integrative process control system,the extracted psychophysiological features are first smoothed by AES scheme.Then,the dimensionality of the smoothed feature vector is reduced by using LPP to enhance the inter-class discrimination capacity.Based on the combination of AES and LPP techniques,the bias-added support vector machine(BSVM)is employed to realize the three-class(low,normal and high)MWL classification.It has been demonstrated that the proposed method can significantly improve the classification accuracy from 88.6%to 99.3%for subject-specific classifier design and from 79.3%to 93.6%for a generic classifier common to all individual subjects.
mental workload,mental stress exponential smoothing locality preservation projection operator functional state
YIN Zhong ZHANG Jianhua
Department of Automation,East China University of Science and Technology,Shanghai,200237,China
国际会议
The 33th Chinese Control Conference第33届中国控制会议
南京
英文
4700-4705
2014-07-28（万方平台首次上网日期，不代表论文的发表时间）