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.

生理信号可用于监测驾驶员的状况，但生理信号的主体间差异会降低监测系统的分类精度。使用测试对象的数据（称为本地数据）对系统进行个性化处理是一种解决方案，但在实际情况下可能无法获取足够的本地数据。因此，本文提出了一种使用小规模本地数据的有效个性化方法。所提出的方法利用模糊支持向量机为局部数据分配比其他数据更高的权重，并通过分析每个数据的重要性为训练数据分配模糊隶属度。介绍了基于生理信号的驾驶员监控系统的三个分类问题，并利用该问题来验证所提出的方法。将分类准确率与其他个性化方法进行比较，结果表明，该方法平均达到了更好的准确率，比使用基本支持向量机的简单方法提高了3.46%，证明了其有效性。所提出的方法可以训练一个个性化的分类器，对测试对象具有更高的准确性。可以利用所提出方法的优点来开发实用的驾驶员监控系统。所提出的方法可以训练一个个性化的分类器，对测试对象具有更高的准确性。可以利用所提出方法的优点来开发实用的驾驶员监控系统。所提出的方法可以训练一个个性化的分类器，对测试对象具有更高的准确性。可以利用所提出方法的优点来开发实用的驾驶员监控系统。