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A hybrid system ensemble based time series signal classification on driver alertness detection

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4 Author(s)
Shen Xu ; Department of Electrical and Computer Engineering, The University of Michigan-Dearborn, 48185, USA ; Ruoqian Liu ; Dai Li ; Yi Lu Murphey

This paper presents the methodologies developed for solving IJCNN 2011's Ford Challenge II problem, where the driver's alertness is to be detected employing physiological, environmental and vehicular data acquired during driving. The solution is based on a thorough four-fold framework consisting of temporal processing, feature creation and extraction, and the training and ensemble of several learning systems, such as neural networks, random forest, support vector machine, trained from diverse features. The selection of input features to a learning machine has always been critique on signal classification. In our approach, the employment of Bayesian network filtered out a set of features and has been proved by the ensemble to be effective. The ensemble technique enhanced the performance of individual systems dramatically. The performance acquired on 30% of the test samples reached an accuracy of 78.34%. These results are significant for a real-world vehicular problem and we are quite confident this solution will become one of the top ones on the competition test data.

Published in:

Neural Networks (IJCNN), The 2011 International Joint Conference on

Date of Conference:

July 31 2011-Aug. 5 2011