Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system

Physiological signals can be utilized to monitor conditions of a driver, but the inter-subject variance of physiological signals can degrade the classification accuracy of the monitoring system. Personalization of the system using the data of a tested subject, called local data, can be a solution, but the acquisition of sufficient local data may not be possible in real situations. Therefore, this paper proposes an effective personalizing method using small-sized local data. The proposed method utilizes a fuzzy support vector machine to allocate higher weight to the local data than to others, and a fuzzy membership is assigned to the training data by analyzing the importance of each datum. Three classification problems for a physiological signal-based driver monitoring system are introduced and utilized to validate the proposed method. The classification accuracy is compared with that of other personalizing methods, and the results show that the proposed method achieves a better accuracy on average, which is 3.46% higher than that of the simple approach using a basic support vector machine, thereby proving its effectiveness. The proposed method can train a personalized classifier with improved accuracy for a tested subject. The advantages of the proposed method can be utilized to develop a practical driver monitoring system.