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使用深度学习进行出行行为评估.

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    此摘要是机器生成的。

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 临床生物力学 临床生物力学

    背景情况:

    • 准确量化患者的出行情况对于康复和临床决策至关重要.
    • 评估行走的传统方法可能是主观的和劳动密集的.
    • 需要客观的,自动化的工具来监测临床环境中的患者流动性.

    研究的目的:

    • 开发和验证基于深度神经网络的系统,用于检测人员和辅助设备.
    • 用计算机视觉和机器学习量化各种出院活动和行为.
    • 为临床医生和住院患者之间共同设定目标提供数据.

    主要方法:

    • 实现了一个定制的深度神经网络对象检测算法.
    • 该系统在临床环境中检测个人和辅助设备.
    • 提取的特征被用作机器学习模型的输入,以量化行走及其模式.

    主要成果:

    • 该系统成功检测到人员和相关辅助设备.
    • 实现了对不同门诊活动和相关行为进行量化.
    • 该系统准确地确定了一个人如何行走以及他们的行走模式.

    结论:

    • 开发的系统提供了一个客观的方法来评估患者的出行情况.
    • 这项技术有助于创建,监控和调整门诊目标.
    • 它支持临床医生和患者在管理流动性方面加强合作.