Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses.
Wearable Driver Distraction Identification On-The-Road via Continuous Decomposition of Galvanic Skin Responses.
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DOI：
10.3390/s18020503
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发表时间：
2018-02-07
期刊：
影响因子：
--
通讯作者：
Taherisadr M
中科院分区：
文献类型：
--
作者：
Dehzangi O;Rajendra V;Taherisadr M
道路上发生致命事故的主要原因之一是分心驾驶。驾驶员个人的持续注意力是完成驾驶任务的必要条件。开车时，一定程度的分心会导致司机失去注意力，这可能导致事故。因此，早期发现分心可以减少事故的数量。许多研究都是为了自动检测司机的注意力分散。尽管基于摄像头的技术已经成功地用于描述驾驶员分心的特征，但侵犯隐私的风险很高。另一方面，生理信号已被证明是驾驶员状态的隐私保护和可靠指标，而采集技术在实际实施中可能会对驾驶员造成干扰。在这项研究中，我们研究了一种连续测量相电皮肤反应（GSR）的方法，该方法使用可穿戴腕带来识别驾驶员在道路驾驶实验中的分心。我们首先利用连续分解分析（CDA）将原始GSR信号分解为相位分量和补相分量，然后对包含皮肤电导信号相关特征的连续相位分量进行进一步分析。我们对非分心和分心（打电话和发短信）场景下的GSR信号进行了高分辨率的光谱-时间变换，以可视化分解后的相位GSR信号与分心场景的相关行为。根据光谱图观察，我们提取了相关的光谱和时间特征，在生理水平上捕捉与分心情景相关的模式。然后，我们使用支持向量机递归特征消除（SVM-RFE）进行特征选择，以便：(1)在受试者群体中生成一个区分特征的秩；(2)在泛化阶段创建一个简化的特征子集，以便在边缘上更有效地识别分心。我们使用支持向量机（SVM）生成10倍交叉验证（10-CV）识别性能度量。实验结果表明，使用所有特征的交叉验证准确率为94.81%，使用简化特征空间的交叉验证准确率为93.01%。SVM-RFE选择的特征集在减少输入特征空间冗余的同时，产生了准确性的边际下降，从而缩短了早期通知驾驶员分心状态所需的响应时间。
One of the main reasons for fatal accidents on the road is distracted driving. The continuous attention of an individual driver is a necessity for the task of driving. While driving, certain levels of distraction can cause drivers to lose their attention, which might lead to an accident. Thus, the number of accidents can be reduced by early detection of distraction. Many studies have been conducted to automatically detect driver distraction. Although camera-based techniques have been successfully employed to characterize driver distraction, the risk of privacy violation is high. On the other hand, physiological signals have shown to be a privacy preserving and reliable indicator of driver state, while the acquisition technology might be intrusive to drivers in practical implementation. In this study, we investigate a continuous measure of phasic Galvanic Skin Responses (GSR) using a wristband wearable to identify distraction of drivers during a driving experiment on-the-road. We first decompose the raw GSR signal into its phasic and tonic components using Continuous Decomposition Analysis (CDA), and then the continuous phasic component containing relevant characteristics of the skin conductance signals is investigated for further analysis. We generated a high resolution spectro-temporal transformation of the GSR signals for non-distracted and distracted (calling and texting) scenarios to visualize the associated behavior of the decomposed phasic GSR signal in correlation with distracted scenarios. According to the spectrogram observations, we extract relevant spectral and temporal features to capture the patterns associated with the distracted scenarios at the physiological level. We then performed feature selection using support vector machine recursive feature elimination (SVM-RFE) in order to: (1) generate a rank of the distinguishing features among the subject population, and (2) create a reduced feature subset toward more efficient distraction identification on the edge at the generalization phase. We employed support vector machine (SVM) to generate the 10-fold cross validation (10-CV) identification performance measures. Our experimental results demonstrated cross-validation accuracy of 94.81% using all the features and the accuracy of 93.01% using reduced feature space. The SVM-RFE selected set of features generated a marginal decrease in accuracy while reducing the redundancy in the input feature space toward shorter response time necessary for early notification of distracted state of the driver.
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影响因子：
3.2
作者：
Young, Kristie L.;Lenne, Michael G.;Williamson, Amy R.
通讯作者：
Williamson, Amy R.
影响因子：
2.3
作者：
Goshvarpour, Atefeh;Abbasi, Ataollah;Daneshvar, Sabalan
通讯作者：
Daneshvar, Sabalan
影响因子：
--
作者：
Huang ML;Hung YH;Lee WM;Li RK;Jiang BR
通讯作者：
Jiang BR
影响因子：
7.5
作者：
CORTES, C;VAPNIK, V
通讯作者：
VAPNIK, V
DOI：
10.1016/j.trf.2011.04.004
发表时间：
2011-09-01
影响因子：
4.1
作者：
Metz, Barbara;Schoemig, Nadja;Krueger, Hans-Peter
通讯作者：
Krueger, Hans-Peter