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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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使用基于CNN的头姿势估计算法量化机动化移动性滑板车中的用户驾驶行为.

Yi Liu, Takenobu Inoue, Jun Suzurikawa

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 12, 2023
    PubMed
    概括

    头部运动可以预测机动化移动性滑板车 (MMSs) 的方向盘,为老年人和移动障碍用户提供更安全的驾驶洞察力. 量化头方向滞后有助于识别危险的驾驶模式.

    科学领域:

    • 老年学是一门学科.
    • 康复工程 康复工程 康复工程
    • 人类因素工程 人类因素工程

    背景情况:

    • 人们越来越担心老年人和残疾人驾驶摩托车 (MMS) 的驾驶安全问题.
    • 现有的研究主要集中在环境危害检测上,对用户行为对MMS驾驶安全的影响的调查有限.

    研究的目的:

    • 开发和实施驾驶状态记录 (DSL) 系统,用于量化MMS操作期间的用户行为.
    • 在MMS驾驶中分析头部运动和方向盘操作之间的时间关系.
    • 探索头部运动作为方向盘操作的预测指数的潜力,并识别危险的驾驶模式.

    主要方法:

    • 开发一种新的驾驶状态记录 (DSL) 系统,以记录用户行为.
    • 应用交叉相关性分析来量化头部运动和方向盘操作之间的时间关系.
    • 收集数据的初步分析,以建立相关性并确定潜在的安全指标.

    主要成果:

    • 开发的DSL系统成功地测量了MMS驾驶期间的用户行为.
    • 在头部运动和方向盘操作之间发现了显著的时间关系.
    • 发现头部运动是预期的方向盘操作的潜在预测因素,并量化了头部方向盘延迟时间.

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    结论:

    • 用户行为显著影响MMS驾驶安全,量化测量是可行的.
    • 头部运动是预测MMS用户方向盘意图的有价值指标.
    • 定量化前方向滞后时间为识别和潜在地减轻危险的驾驶模式提供了一种新的方法,提高整体MMS安全性.