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一种基于人类生命参数融合的道路催眠识别方法

Bin Wang¹, Jingheng Wang², Xiaoyuan Wang¹

¹College of Electromechanical Engineering, Qingdao University of Science and Technology, Qingdao 266000, China.

Sensors (Basel, Switzerland)

|December 17, 2024

概括

这项研究引入了一种新的方法,通过将眼球运动和脑电图 (EEG) 数据结合起来来识别道路催眠. 开发的模型有效地检测道路催眠,提高驾驶员的安全.

科学领域:

神经科学是一个神经科学.
人与计算机的交互
运输安全运输安全

背景情况:

道路催眠是一种危险的状态,在驾驶过程中意识下降.
识别道路催眠依赖于外部 (例如,眼睛的运动) 和内部 (例如,脑电图 - EEG) 的特征.
现有的方法可能无法完全捕捉道路催眠的复杂性质.

研究的目的:

通过融合人类生活参数,提出一种新的道路催眠识别方法.
开发一个强大的模型,整合眼睛运动和EEG数据,以提高检测准确度.
为了增强对道路催眠背后的机制的理解.

主要方法:

从驾驶实验中收集的眼动和EEG数据.
使用主要组件分析 (PCA) 和独立组件分析 (ICA) 预处理的数据.
用自我注意模型 (SAM) 训练眼动数据和用深度信念网络 (DBN) 进行EEG数据,然后使用堆叠进行融合.

主要成果:

合并模型有效地识别了道路催眠.
拟议的方法在识别这种驾驶状态方面表现出了很高的准确性.
重复随机子样本交叉验证 (RRSCV) 证实了模型的可靠性.

结论:

眼睛运动和EEG数据的融合为道路催眠识别提供了有效的方法.
开发的SAM-DBN堆叠模型显著提高了检测准确度.
这项研究有助于理解道路催眠和提高驾驶员安全系统.

关键词:

司机司机司机司机人类生活参数人类生活参数道路催眠催眠道路催眠国家识别状态识别.车辆车辆车辆车辆车辆

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