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基于Kinect骨数据的人体姿势识别的循环网络解决方案.

Bruna Maria Vittoria Guerra1, Stefano Ramat1, Giorgio Beltrami1

  • 1Laboratory of Bioengineering, Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy.

Sensors (Basel, Switzerland)
|June 10, 2023
PubMed
概括
此摘要是机器生成的。

这项研究探讨了用于监测日常生活姿势的循环神经网络 (RNN),使用来自RGB-D传感器的骨架数据. 一个具有数据增强的3BGRU模型在检测姿势和危险情况方面取得了88%的准确性.

关键词:
环境辅助生活环境辅助生活深度学习是一种深度学习.人类行动承认承认经常性的神经网络.骨架数据 骨架数据

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 环境辅助生活 (AAL) 系统增强了个人,特别是脆弱的人的日常生活支持.
  • 对于AAL中的摄像机的隐私问题,RGB-D设备可以提取骨架数据来解决.
  • 深度学习,特别是循环神经网络 (RNN),显示了分析骨数据以识别人类姿势的前景.

研究的目的:

  • 评估两个RNN模型 (2BLSTM和3BGRU) 的性能,用于识别日常生活姿势和危险情况.
  • 为了比较人造动力学特征与原始骨关节坐标的有效性.
  • 调查数据增强对家庭监控模型概括的影响.

主要方法:

  • 利用Kinect V2设备的3D骨架数据进行姿势识别.
  • 训练并测试了两个RNN模型:2BLSTM和3BGRU.
  • 对比了两个特征集:8个动力特征和52个与距离的联合坐标.
  • 应用数据增强到3BGRU模型,以平衡训练数据集.

主要成果:

  • 当与数据增强相结合时,3BGRU模型实现了88%的准确性.
  • 在两个测试的功能集之间,性能有所不同.
  • 这项研究表明,在AAL应用中,在骨架数据上使用RNN的可行性.

结论:

  • 具有数据增强的3BGRU模型显示了在AAL.中准确地识别姿势和活动的巨大潜力.
  • 使用深度学习的骨架数据分析为家庭监控提供了一种保护隐私的方法.
  • 进一步的研究可以优化功能提取和模型架构,用于增强的AAL系统.