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基于深度学习的开放式人体识别,使用雷达提取心脏信号.

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    此摘要是机器生成的。

    这项研究介绍了一种新的深度学习算法,用于使用生命体征的基于雷达的人身份识别. 它在近距离和开放条件下都能达到高精度,超过现有方法.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 人工智能的人工智能
    • 信号处理 信号处理

    背景情况:

    • 使用雷达进行非接触式生命体征测量正在获得引力.
    • 现有的个人识别方法经常与测试数据包括未见的类别的开放条件作斗争.

    研究的目的:

    • 开发一种新的深度学习算法,用于使用雷达提取的生命体征来识别人.
    • 为了应对现实世界的场景中开放式的人身份识别的挑战.

    主要方法:

    • 从多普勒雷达回声中提取心跳信号.
    • 使用两个卷积神经网络 (CNN) 模型的转移学习.
    • 利用分发模型进行校准和战略决策流程来组合模型输出.

    主要成果:

    • 在近距离条件下实现了99.61%的识别准确性.
    • 在具有挑战性的开放设置条件下证明了94.35%的识别准确性.
    • 性能优于现有的个人识别方法.

    结论:

    • 拟议的深度学习算法为基于雷达的个人识别提供了一个强大的解决方案.
    • 该方法对现实世界的应用具有显著的前景,特别是在开放式场景中.