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基于深度学习的眼神控制轮椅

Jun Xu1, Zuning Huang2, Liangyuan Liu2

  • 1School of Automation, Harbin University of Science and Technology, Harbin 150080, China.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

这项研究为ALS患者引入了一种通过眼睛运动控制的智能轮椅. 该系统在眼动识别方面达到98.49%的准确性,使轮椅导航能够顺利和可控.

科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能的人工智能
  • 康复技术 康复技术 康复技术

背景情况:

  • 患有肌缩侧面硬化症 (ALS) 的患者经常面临渐进的运动功能丧失,限制了他们的移动性和独立性.
  • 现有的辅助技术可能无法完全满足ALS患者在自然环境中的复杂需求.

研究的目的:

  • 设计和开发一个智能轮椅系统,用于ALS患者的眼动控制.
  • 增强辅助轮椅的自然环境导航能力.
  • 为了提高轮椅运动的流性和响应性.

主要方法:

  • 整合了电动轮椅,视觉系统,2D机器人手臂和主控制系统.
  • 使用单眼相机捕捉眼睛图像,以深度学习为基础,以注意力机制识别眼睛运动方向.
  • 基于操纵杆轨迹和轮椅速度的运动加速模型的开发,以确保平稳的运动.
  • 在嵌入式AI控制器上部署轻量级的眼动识别模型.

主要成果:

  • 在眼睛运动方向识别方面获得了98.49%的准确性.
  • 启用轮椅的运动速度高达1米/秒.
  • 在没有突然加速的情况下展示了轮椅流的运动轨迹.
关键词:
这是CBAM的注意力.深度学习是一种深度学习.用眼睛追踪来进行追踪.轮椅加速模型的模型.

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结论:

  • 开发的智能轮椅系统有效地利用ALS患者的眼动控制.
  • 该系统增强了在自然环境中的流动性和独立性.
  • 实施的运动加速模型显著提高了轮椅操作的流性.