基于四大树的驱动器分类使用深度学习来检测轻度认知障碍
Seyedeh Gol Ara Ghoreishi1, Charles Boateng1, Sonia Moshfeghi1
1Florida Atlantic University, Boca Raton, USA.
IEEE access : practical innovations, open solutions
|April 21, 2025
概括
这项研究引入了一种新的四树方法来对驾驶员进行分类,以检测轻度认知障碍 (MCI). 该方法有效地分析驾驶模式,实现高精度,改善道路安全和认知健康监测.
科学领域:
- 计算神经科学是一种计算神经科学.
- 运输工程 运输工程 运输工程
- 机器学习用于医疗保健
背景情况:
- 检测轻度认知障碍 (MCI) 对于及时干预和患者护理至关重要.
- 分析驾驶模式为认知健康评估提供了一种非侵入性方法.
- 现有的驾驶员分类方法面临着大型,复杂的GPS轨迹数据的挑战.
研究的目的:
- 开发一种有效的方法来使用GPS数据对轻度认知障碍 (MCI) 的司机进行分类.
- 为分析空间驾驶模式提出一个新的地理区域四树结构.
- 通过高级特征表示和深度学习来提高驾驶员分类准确性.
主要方法:
- 利用了运输网络上的GPS点的真实世界数据集.
- 开发了一个地理区域四树结构来表示驾驶轨迹的空间层次结构.
- 为输入卷积神经网络 (CNN) 设计了新的驱动功能.
- 实现了一个基于四树的驱动器分类 (QBDC) 算法.
主要成果:
- 拟议的基于四树的驾驶员分类 (QBDC) 算法获得了95%的F1分数.
- 与基线模型相比,表现出显著的性能改善.
- 验证了地理区域四树在从驾驶模式中提取可解释特征方面的有效性.
结论:
- 地理区域四树结构对于描述复杂的驾驶模式和对司机的分类是有效的.
- 拟议的方法显示了改善道路安全和认知健康监测的巨大潜力.
- 这种方法通过驾驶行为分析,为早期发现认知衰退提供了一个有希望的途径.
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