Deep Physiological Arousal Detection in a Driving Simulator Using Wearable Sensors

Aaqib Saeed, S. Trajanovski, M. V. Keulen, J. V. Erp
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引用次数: 24

Abstract

Driving is an activity that requires considerable alertness. Insufficient attention, imperfect perception, inadequate information processing, and sub-optimal arousal are possible causes of poor human performance. Understanding of these causes and the implementation of effective remedies is of key importance to increase traffic safety and improve driver's well-being. For this purpose, we used deep learning algorithms to detect arousal level, namely, under-aroused, normal and over-aroused for professional truck drivers in a simulated environment. The physiological signals are collected from 11 participants by wrist wearable devices. We presented a cost effective ground-truth generation scheme for arousal based on a subjective measure of sleepiness and score of stress stimuli. On this dataset, we evaluated a range of deep neural network models for representation learning as an alternative to handcrafted feature extraction. Our results show that a 7-layers convolutional neural network trained on raw physiological signals (such as heart rate, skin conductance and skin temperature) outperforms a baseline neural network and denoising autoencoder models with weighted F-score of 0.82 vs. 0.75 and Kappa of 0.64 vs. 0.53, respectively. The proposed convolutional model not only improves the overall results but also enhances the detection rate for every driver in the dataset as determined by leave-one-subject-out cross-validation.
基于可穿戴传感器的驾驶模拟器深层生理唤醒检测
开车是一项需要高度警觉的活动。注意力不足、感知不完善、信息处理不充分和次优唤醒都是人类表现不佳的可能原因。了解这些原因并实施有效的补救措施对于提高交通安全和改善驾驶员的健康至关重要。为此,我们使用深度学习算法来检测模拟环境中专业卡车司机的唤醒水平,即唤醒不足、正常和过度唤醒。通过手腕上的可穿戴设备收集11名参与者的生理信号。我们提出了一种成本效益高的基于主观测量睡意和压力刺激得分的唤醒基础真相生成方案。在这个数据集上,我们评估了一系列用于表示学习的深度神经网络模型,作为手工特征提取的替代方案。我们的研究结果表明,在原始生理信号(如心率、皮肤电导和皮肤温度)上训练的7层卷积神经网络优于基线神经网络和去噪自编码器模型,加权f分数分别为0.82比0.75,Kappa分别为0.64比0.53。所提出的卷积模型不仅改善了整体结果,而且提高了数据集中每个驾驶员的检测率,这是由留一个主体的交叉验证确定的。
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