Sensor-based driver condition recognition using support vector machine for the detection of driver drowsiness

Driver's drowsiness is one of the main causes of traffic accidents. It accounts for up to 20% of serious or fatal accidents on the roads. In this paper, we propose a novel sensor-based driver condition recognition to prevent drowsiness-related accidents by using Support Vector Machine (SVM). It helps to determine the driver's condition by using sensors of the wearable device. The process for the proposed warning system is as follows: First, we acquire bio-data from a PhotoPlethysmoGraphy(PPG) sensor in the device to understand the characteristics of the driver's condition. Then, the acquired data is processed through segmentation and averaging to increase classification accuracy. The processed data is used as feature vectors in the SVM for the driver's condition classification. To evaluate performance of proposed method, the following metrics were used: accuracy, error rate, precision and recall. From the calculated results, the proposed method had high accuracy of 96.3 %. Finally, we could create the driver's condition recognition model that can be applied to a system which can alert the driver.