Tsuchida Ayumi, Bhuiyan Md, Oguri Koji
Graduate school of Information Science and Technology, Aichi Prefectural University, Aichi 480-1198, Japan.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:2543-6. doi: 10.1109/IEMBS.2009.5334766.
This paper presents a novel method that uses eyelid closure and heart rate variability to estimate the driver's drowsiness level. Laboratory experiments were conducted by using a proprietary driving simulator, which induced drowsiness among the test drivers. The purposes of these experiments were to obtain the electrocardiogram (ECG) and the eye-blink video sequences. Also the drivers were monitored through a video camera. The changes in facial expression of the drivers were used as a standard index of drowsiness level. Error-Correcting Output Coding (ECOC) was employed as a multi-class classifier to estimate the drowsiness level. We extended the ordinary ECOC using a loss function for decoding procedure to obtain class tendencies of each drowsiness level. We used the Loss-based Decoding ECOC (LD-ECOC) to classify the drowsiness level. As a result, we obtained an extraordinarily high accuracy for estimation of drowsiness level.
本文提出了一种利用眼睑闭合和心率变异性来估计驾驶员困倦程度的新方法。通过使用专有的驾驶模拟器进行实验室实验，该模拟器会使测试驾驶员产生困倦感。这些实验的目的是获取心电图（ECG）和眨眼视频序列。此外，还通过摄像机对驾驶员进行监测。驾驶员面部表情的变化被用作困倦程度的标准指标。采用纠错输出编码（ECOC）作为多类分类器来估计困倦程度。我们使用一个用于解码过程的损失函数对普通ECOC进行扩展，以获得每个困倦程度的类别倾向。我们使用基于损失的解码ECOC（LD-ECOC）对困倦程度进行分类。结果，我们在困倦程度估计方面获得了极高的准确率。
