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一个功能增强的变压器模型来识别来自野生智能手表数据的功能活动.

Bryan Minor, Colin Greeley, Ryan Holder

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    此摘要是机器生成的。

    这项研究引入了一种使用可穿戴传感器的功能人类活动识别 (HAR) 的新方法,改进了健康评估. 该方法增强了特征表示,以便更好地分类复杂的日常行为.

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    科学领域:

    • 人与计算机的交互
    • 穿戴式计算可以穿戴.
    • 数字健康数字健康

    背景情况:

    • 传统的人类活动识别 (HAR) 专注于原子运动.
    • 识别功能活动对于医疗保健至关重要,但在现实环境中是复杂的.
    • 现有的方法在野外功能性HAR的可变性和数据稀疏性中扎.

    研究的目的:

    • 调查功能性HAR的方法.
    • 引入一种新的方法,使用特征令牌转换器嵌入来增强特征表示.
    • 为了提高复杂,目标导向行为的分类性能.

    主要方法:

    • 比较了各种机器学习和深度学习方法的功能性HAR.
    • 开发了一种使用特征令牌转换器嵌入的新方法.
    • 使用大规模的ArWISE数据集 (n=503,32M+标记点) 进行纵向收集.

    主要成果:

    • 功能嵌入显著改善了功能性HAR模型性能.
    • 提出的方法在处理现实世界的变化和数据稀疏性方面表现出有效性.
    • 实验表明,在多样化的人口中,一般化能力是很强的.

    结论:

    • 整合特征嵌入对于功能性HAR来说是有利的.
    • 这项工作弥合了原子运动和功能性行为识别之间的差距.
    • 这些发现支持在数字健康和以人为中心的人工智能领域的先进,行为意识的应用.