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Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
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基于神经网络的加速器对粒度活动进行识别:评估跨多种移动性配置文件的概括性.

Metin Bicer1,2, James Pope3, Lynn Rochester1,2

  • 1Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE1 7RU, UK.

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概括
此摘要是机器生成的。

这项研究表明,封闭式循环单元在使用可穿戴传感器的人类活动识别 (HAR) 中表现出色,在不同年龄组中实现高精度. 结合多样化的培训数据进一步提高了老年人的表现.

关键词:
深度学习是一种深度学习.人类活动的认可 人类活动的认可可穿戴式传感器传感器

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科学领域:

  • 生物医学工程 生物医学工程
  • 计算机科学 计算机科学
  • 老年学是指老年学的学科.

背景情况:

  • 人类活动识别 (HAR) 对于数字医疗保健至关重要,但传统方法与现实世界的数据变化和跨队列概括性作斗争.
  • 将HAR系统适应不同人群,例如年轻人与老年人群,仍然是一个重大挑战.

研究的目的:

  • 使用可穿戴传感器数据调查 HAR,重点是年轻人和老年人之间的交叉队列评估.
  • 评估神经网络架构,采样频率和传感器位置对HAR性能的影响.
  • 确定适用于不同年龄组的现实活动识别的可靠方法.

主要方法:

  • 利用来自大腿和腰部传感器 (50 Hz) 的加速度计数据捕捉日常活动,与视频记录同步.
  • 在年轻队列数据上训练神经网络,特别封闭的循环单元 (GRU),并在老队列数据上进行测试.
  • 使用准确度,回忆,精度,F1分数和混矩阵评估性能,分析网络架构,采样频率和传感器放置的影响.

主要成果:

  • 封闭的循环单元架构表现出卓越的性能,年轻群体的加权F1得分为0.95 ± 0.05,老群体的加权F1得分为0.93 ± 0.05.
  • 部安装的传感器通常表现优于腰部传感器,除了"躺"活动外.
  • 通过增加训练数据的可变性,组合数据集显著改善了旧队列的性能 (加权F1得分:0.97 ± 0.02).

结论:

  • 神经网络,特别是GRU,为跨年龄队伍的强大的HAR提供了一个高度通用的方法.
  • 网络架构和数据集组成是成功 HAR 的关键因素.
  • 基于传感器的可穿戴HAR显示了数字医疗保健应用的巨大潜力,尤其是在考虑队列多样性时.