Au-Zone Technologies Inc., Calgary, AB, Canada.
Alcohol Countermeasure Systems Corp. (ACS), 60 International Boulevard, Toronto, ON, Canada.
Accid Anal Prev. 2021 Jun;156:106107. doi: 10.1016/j.aap.2021.106107. Epub 2021 Apr 10.
Fatigue negatively affects the safety and performance of drivers on the road. In fact, drowsiness and fatigue are the cause of a substantial number of motor vehicle accidents. Drowsiness among the drivers can be detected using variety of modalities, including electroencephalogram (EEG), eye movement, and vehicle driving dynamics. Among these EEG is highly accurate but very intrusive and cumbersome. On the other hand, vehicle driving dynamics are very easy to acquire but accuracy is not very high. Eye movement based approach is very attractive in terms of balance between these two extremes. However, eye movement based techniques normally require an eye tracking device which consists of high speed camera with sophisticated algorithm to extract eye movement related parameters such as blinking, eye closure, saccades, fixation etc. This makes eye tracking based drowsiness detection difficult to implement as a practical system, especially on an embedded platform. In this paper, authors propose to use eye images from camera directly without the need for expensive eye-tracking system. Here, eye related movements are captured by Recurrent Neural Network (RNN) to detect the drowsiness. Long Short Term Memory (LSTM) is a class of RNN which has several advantages over vanilla RNNs. In this work an array of LSTM cells are utilized to model the eye movements. Two types of LSTMs were employed: 1-D LSTM (R-LSTM) which is used as baseline and the convolutional LSTM (C-LSTM) which facilitates using 2-D images directly. Patches of size 48 × 48 around each eye were extracted from 38 subjects, participating in a simulated driving experiment. The state of vigilance among the subjects were independently assessed by power spectral analysis of multichannel electroencephalogram (EEG) signals, recorded simultaneously, and binary labels of alert and drowsy (baseline) were generated. Results show high efficacy of the proposed system. R-LSTM based approach resulted in accuracy around 82 % and C-LSTM based approach resulted in accuracy in the range of 95%-97%. Comparison is also provided with a recently published eye-tracking based approach, showing the proposed LSTM technique outperform with a wide margin.
疲劳会对道路上驾驶员的安全和表现产生负面影响。事实上，瞌睡和疲劳是大量机动车事故的原因。可以使用多种模式来检测驾驶员的瞌睡状态，包括脑电图（EEG）、眼动和车辆行驶动态。在这些模式中，脑电图非常准确，但非常侵入性和繁琐。另一方面，车辆行驶动态非常容易获取，但准确性不是很高。基于眼动的方法在这两个极端之间具有吸引力。然而，基于眼动的技术通常需要一个眼动跟踪设备，该设备由高速摄像机和复杂的算法组成，以提取眼动相关参数，如眨眼、眼睛闭合、扫视、注视等。这使得基于眼动的瞌睡检测难以实现为实际系统，特别是在嵌入式平台上。在本文中，作者提出直接使用来自相机的眼图像，而无需昂贵的眼动跟踪系统。在这里，通过循环神经网络（RNN）捕获与眼睛相关的运动，以检测瞌睡状态。长短期记忆网络（LSTM）是 RNN 的一种类型，它比传统 RNN 具有多个优势。在这项工作中，使用一系列 LSTM 细胞来模拟眼动。使用了两种类型的 LSTM：用于基线的 1 维 LSTM（R-LSTM）和直接使用 2 维图像的卷积 LSTM（C-LSTM）。从参与模拟驾驶实验的 38 名受试者的每只眼睛周围提取大小为 48×48 的斑块。同时记录多通道脑电图（EEG）信号的功率谱分析，独立评估受试者的警觉状态，并生成警觉和瞌睡（基线）的二进制标签。结果表明该系统具有高效性。基于 R-LSTM 的方法的准确率约为 82%，基于 C-LSTM 的方法的准确率在 95%-97%范围内。还与最近发表的基于眼动的方法进行了比较，表明所提出的 LSTM 技术具有很大的优势。
