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实时传感器嵌入的神经网络用于人类活动识别.

Ali Shakerian1, Victor Douet1, Amirhossein Shoaraye Nejati1

  • 1Department of Electrical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada.

Sensors (Basel, Switzerland)
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PubMed
概括

这项研究介绍了一种新的胸部佩戴传感器,用于实时人类活动识别 (HAR). 它使用低成本微控制器上的嵌入式神经网络进行准确的设备上活动预测.

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
  • 机器学习 机器学习

背景情况:

  • 人类活动识别 (HAR) 对医疗保健和人机交互至关重要.
  • 现有的HAR系统通常依赖于外部处理,限制实时应用.
  • 需要高效的设备内 HAR 解决方案.

研究的目的:

  • 推出一种新的,独立的传感器,用于实时识别人类活动.
  • 为了证明在HAR的低成本微控制器上实施卷积神经网络 (CNN) 的可行性.
  • 评估嵌入式系统用于预测人类行为的性能.

主要方法:

  • 开发一个可穿戴传感器,集成一个惯性测量单元 (IMU) 和一个低成本的微控制器.
  • 在微控制器上直接实现卷积神经网络 (CNN),用于实时数据处理.
  • 从IMU获得实时数据采集和使用嵌入式CNN进行活动预测.

主要成果:

  • 拟议的传感器可以准确地实时检测和预测人类活动.
  • 嵌入式CNN在低成本的微控制器上实现了高推断性能.
  • 该系统成功地消除了对外部处理设备的需求.
关键词:
卷积神经网络 (CNN) 是一种神经网络.人类活动识别 (HAR)微控制器上的微控制器实时实时的时间.

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结论:

  • 开发的传感器为嵌入式,实时的人类活动识别提供了准确和高效的解决方案.
  • 这种方法可以在没有外部计算资源的情况下实现实际的设备内 HAR 应用程序.
  • 这些发现突显了边缘计算在高级活动监控方面的潜力.