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基于深度学习的主体独立的人类活动识别使用智能锁数据

Najmeh Movahhed Neya, Edward Sazonov, Xiangrong Shen

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了用于人类活动识别 (HAR) 的智能锁锁设备,在使用深度学习对行走和爬楼梯等活动进行分类时达到98.4%的准确性. 新型传感器融合增强了HAR在医疗保健和康复领域的应用.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 机器学习 机器学习
    • 可穿戴技术可穿戴技术

    背景情况:

    • 人类活动识别 (HAR) 对医疗保健和康复应用至关重要.
    • 传统的HAR系统通常依赖于惯性测量单位 (IMU) 数据.
    • 需要一些不引人注目的有效的HAR解决方案.

    研究的目的:

    • 介绍和评估一种新的可穿戴设备,智能锁锁,用于基于深度学习的HAR.
    • 探索将IMU和负载单元数据相结合的有效性,以改善活动分类.
    • 开发和验证用于HAR的卷积神经网络 (CNN) 模型,使用来自智能锁锁的数据.

    主要方法:

    • 开发了一种新的智能锁锁设备,集成IMU和负载细胞传感器.
    • 从8名参与者中收集了各种活动 (步行,爬楼梯/下楼) 的数据.
    • 设计和训练了一种CNN模型,包含卷积,最大聚合,ReLU,正常化,脱落和密集层.

    主要成果:

    • 拟议的CNN模型在收集的数据集上实现了98.4%的高平均识别精度.
    • 使用Leave-one-out (L1O) 验证技术来评估模型概括性.
    • 智能锁锁设备证明了识别不同的人类活动的可行性.

    结论:

    • 智能锁锁设备是人类活动识别的可行和有效工具.
    • IMU和负载单元数据的集成增强了HAR的功能.
    • 对智能锁锁在医疗保健和康复中的应用进行进一步的研究是有必要的.