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基于使用卷积神经网络的智能图像分析的动态跟踪和实时摔倒检测.

Ching-Bang Yao1, Cheng-Tai Lu1

  • 1Department of Information Management, Chinese Culture University, Taipei 11114, Taiwan.

Sensors (Basel, Switzerland)
|December 17, 2024
PubMed
概括

这项研究介绍了一种人工智能驱动的无人机系统,用于老年护理中的实时摔倒检测. 改进后的系统准确地识别了落情况,提高了护理接收者的安全.

科学领域:

  • 老年学和人工智能的人工智能
  • 计算机视觉和人机交互的人机交互

背景情况:

  • 全球人口老龄化增加了对老年护理服务的需求.
  • 目前的护理人力不足以满足不断增长的护理需求.
  • 实时跌倒检测和分析对于老年人的安全至关重要.

研究的目的:

  • 开发一种人工智能系统,用于实时跟踪和检测护理接收者的落.
  • 通过无人机移动性和先进的算法来提高下降分析的准确性.
  • 提高系统评估各种落场景的能力.

主要方法:

  • 无人机移动性与Dlib HOG算法的集成,用于实时跟踪.
  • 增强OpenPose用于在下降场景中进行多人行动分析.
  • 开发智能跌倒姿势分析,以准确评估情况.

主要成果:

  • 与谷歌可教机器的Pose项目相比,该系统在四个下降方向上实现了更高的识别精度.
  • 回落识别的准确性从70.35%大幅提高到95%.
  • 前进和向左跌倒的识别准确度增加了近14%,在各种场景中超过95%.

结论:

关键词:
打开Pose,可以使用Pose.面部识别功能 面部识别功能跌倒姿势分析分析实时跟踪跟踪实时跟踪智能家居护理 智能家庭护理

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  • 开发的AI系统在检测和分析落方面表现出卓越的准确性.
  • 无人机和增强的人工智能算法的集成为老年护理安全提供了一个有希望的解决方案.
  • 这项技术可以显著改善实时监测和应对老龄化人口下降的情况.