一种混合方法来检测驾驶员的昏昏欲睡,利用实际数据来提高系统的性能和适用性.
Farin Khanehshenas1, Adel Mazloumi1,2, Ali Nahvi3
1Department of Occupational Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Work (Reading, Mass.)
|November 26, 2023
概括
这项研究开发了一种使用呼吸信号和车辆数据的实用驾驶员嗜睡检测系统. 该系统实现了高精度,为现实世界的应用提供了可行的解决方案.
科学领域:
- 人与计算机的交互
- 生物医学工程 生物医学工程
- 运输安全运输安全
背景情况:
- 现有的驾驶员昏昏欲睡检测系统缺乏实时适用性.
- 需要改进,实用解决方案来监测驾驶员的警觉性.
研究的目的:
- 调查驾驶员昏昏欲睡检测系统的可行性.
- 整合呼吸信号,车辆侧面位置和反应时间,以提高性能.
- 探索车外数据收集方法的现实应用.
主要方法:
- 在驾驶模拟器中收集了25名志愿者的数据.
- 使用可穿戴传感器进行呼吸活动和模拟器测试,以测试反应时间和车辆位置.
- 应用统计测试以确定区分警觉和昏昏状态的重要特征.
- 采用机器学习分类器,包括支持矢量机器 (SVM) 和长短期内存 (LSTM).
主要成果:
- 支持矢量机 (SVM) 分类器实现了88%的准确性.
- 关键性能指标包括85%的精度,83%的回忆率和84%的F1分数.
- 选择的功能有效地区分了警觉和昏昏欲睡的驾驶员状态.
结论:
- 这项研究表明,极其精确的驾驶员昏昏欲睡检测有潜力.
- 实际的驾驶员昏昏欲睡系统是可行的,使用组合,更少的侵入性测量.
- 车外数据收集方法提高了系统的适用性.
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