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用生理信号检测多层次驾驶员疲劳的深度学习:一种全面的方法

Mohammad Peivandi¹, Sevda Zafarmandi Ardabili², Sobhan Sheykhivand³

¹Department of Biomedical Engineering, Wayne State University, Detroit, MI 48202, USA.

Sensors (Basel, Switzerland)

|October 14, 2023

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这项研究引入了一种新的方法,用于检测驾驶员疲劳,使用生理信号和先进的人工智能. 开发的系统准确地识别了多个疲劳水平,提高了驾驶员的安全.

科学领域:

工程工程师工程师工程师
计算机科学计算机科学
神经科学是一个神经科学.

背景情况:

司机疲劳是交通事故的重要原因之一.
现有的疲劳检测方法通常依赖于自我报告,这可能是不可靠的.

研究的目的:

开发和评估一种新的多层次驾驶员疲劳检测方法.
创建一个强大的系统来识别和分类不同状态的驾驶员疲劳.

主要方法:

使用生理信号 (ECG,EEG,EMG,呼吸力) 创建了一个多层次的驾驶员疲劳数据库.
用于特征提取和分类,采用了结合对抗生成网络和卷积神经网络的定制端到端架构.
在架构中使用了2型模糊集来减轻不确定性,取代了传统的激活功能.

主要成果:

该系统在检测不同级别的驾驶员疲劳方面取得了很高的准确性:两级疲劳检测96.8%,三级疲劳检测95.1%,五级疲劳检测89.1%.
使用EEG信号进行处理,得到其他生理学数据的支持,提供了可靠的疲劳评估.
对2型模糊集的调查证明了它们在提高模型性能方面的有效性.

结论:

关键词:

在美国,CNN是CNN.这是一个EEGEEGEEGEEGEEGEEGEEG.没有了,没有了,没有了.深度学习是一种深度学习.驾驶员疲劳导致的疲劳功能提取特性提取机器学习是机器学习.生理信号生理信号

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拟议的模型在识别不同级别的驾驶员疲劳方面表现出最佳性能,并且具有很高的准确性.

这种先进的驾驶员疲劳检测系统具有很大的潜力,可以在车辆安全系统中实际实施,以警告驾驶员.

生理信号和复杂的人工智能的整合为未来的驾驶员监控技术提供了一个有希望的方向.