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物联网驱动的RNN用于改进人类活动识别,并增强本地化和分类.

Naif Al Mudawi1, Usman Azmat2, Abdulwahab Alazeb1

  • 1School Department of Computer Science, College of Computer Science and Information System, Najran University, Najran, 55461, Saudi Arabia.

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概括

这项研究引入了一个强大的系统,用于人类活动识别 (HAR) 和使用噪音传感器数据的定位. 这种新的方法在识别活动和地点方面取得了很高的准确性,优于现有的方法.

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科学领域:

  • 计算机科学 计算机科学
  • 信号处理 信号处理
  • 机器学习 机器学习

背景情况:

  • 人类活动识别 (HAR) 和本地化是智能设备驱动的关键研究领域.
  • 智能设备的传感器数据往往含有大量的噪音,需要强大的系统设计.

研究的目的:

  • 开发一种无噪声和高效的系统,用于识别和定位人类活动.
  • 为了利用多个算法提高HAR和本地化任务的性能.

主要方法:

  • 使用切比舍夫I型过器对信号进行无声化,然后进行窗口化.
  • 对活动和位置进行并行特征提取,使用Boruta算法进行特征选择.
  • 用粒子群优化 (PSO) 优化数据优化和平行训练用于HAR和本地化的反复神经网络 (RNNs).

主要成果:

  • 该系统在超感官和萨塞克斯华为运动 (SHL) 数据集上表现出了卓越的性能.
  • 在超感官上的HAR获得了89.25%和90.50%的精度,在SHL上的HAR获得了95.75%的精度.
  • 在超感官和SHL上分别实现了95.75%和91.50%的定位精度,超过了最先进的方法.

结论:

  • 拟议的系统有效地处理噪音传感器数据,以准确地识别和定位人类活动.
  • 过,特征选择,优化和并行RNN的综合方法提供了一个强大的解决方案.
  • 该系统在基准数据集上的卓越性能验证了其对现实世界的应用的有效性和潜力.