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无线人体区域传感器网络基于使用深度学习识别人类活动.

Ehab El-Adawi1, Ehab Essa2, Mohamed Handosa1

  • 1Department of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, 35516, Egypt.

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|February 1, 2024
PubMed
概括
此摘要是机器生成的。

本研究引入了一个新的人类活动识别 (HAR) 系统,在无线人体区域网络 (WBAN) 中使用格拉米安角场 (GAF) 和DenseNet. 这种新的方法可以实现高精度的患者健康监测.

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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
  • 人工智能的人工智能

背景情况:

  • 无线身体区域网络 (WBAN) 对于远程监测患者,收集健康状况和活动数据至关重要.
  • 基于传感器的人类活动识别 (HAR) 正因其隐私和便利性而获得吸引力,物联网和可穿戴技术放大了这一点.
  • 深度学习在自动特征提取方面表现出色,但环境因素等挑战会影响传统的计算机视觉方法.

研究的目的:

  • 为WBANs提出和开发一个先进的HAR系统.
  • 利用格拉米安角场 (GAF) 和DenseNet来提高HAR的准确性.
  • 在现实世界WBAN场景中解决现有的HAR方法的局限性.

主要方法:

  • 来自WBAN的时间序列传感器数据进行了预处理,以删除文物并应用中位过.
  • 格拉米安角场 (GAF) 算法将时间序列数据转换为2D图像.
  • 使用DenseNet进行自动特征提取和集成多传感器数据.

主要成果:

  • 拟议的GAF和基于DenseNet的HAR系统表现出卓越的性能.
  • 获得了97.83%的准确性.
  • 记录了97.83%的F测量值和97.64%的马修斯相关系数 (MCC).

结论:

  • 开发的HAR系统有效地利用GAF和DenseNet在WBAN中准确识别患者活动.
  • 这种方法为医疗监测提供了一个强大的解决方案,克服了共同的挑战.
  • 高精度表明临床应用和增强患者护理的巨大潜力.