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一个可训练的开源机器学习加速器活动识别工具箱:深度学习方法.

Fluri Wieland1, Claudio Nigg1

  • 1Department of Health Science, Institute of Sports Science, University of Bern, Bern, Switzerland.

JMIR AI
|June 14, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了HumanActivityRecorder,这是一个开源智能手机工具,用于准确识别人类活动. 它使用深度学习来实现87%的准确性对行为进行分类,提高科学研究的重复性.

关键词:
加速测量仪加速测量仪活动分类活动分类.活动识别活动识别.活动记录器活动记录器深度学习是一种深度学习.深度学习算法深度学习算法数字健康应用程序 数字健康应用程序机器学习是机器学习.这是开源的,开源的.传感器设备 传感器设备 传感器设备智能手机应用程序 智能手机应用程序

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科学领域:

  • 人与计算机的互动.
  • 生物医学工程 生物医学工程
  • 机器学习用于医疗保健

背景情况:

  • 当前的活动跟踪器缺乏科学研究所需的准确性和开源性质.
  • 现有的运动确定软件对于精确的科学应用是不够的.

研究的目的:

  • 开发一个准确,可训练和开源的基于智能手机的活动跟踪工具箱.
  • 为了研究应用,创建一个适应新行为的系统.

主要方法:

  • 使用了一种半监督的深度学习方法.
  • 活动分类是基于加速度计和陀螺仪数据.
  • 该模型使用专有和公共数据集进行了训练和验证.

主要成果:

  • 人类活动记录器在分类6种不同的行为方面取得了大约87%的准确性.
  • 开发的算法表现出优越性,而不是一个尺寸适应的神经架构模型.
  • 证实了对采样速度和传感器尺寸变化的稳定性.

结论:

  • 人类活动记录器 (HumanActivityRecorder) 为活动跟踪提供了一种多功能,可回收和准确的开源解决方案.
  • 该工具箱促进了适应特定的研究行为,并提高了科学研究的重复性.
  • 对新数据的持续测试确保了系统的持续实用性和准确性.