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基于EEG运动图像和视觉感知融合的家庭机器人互动.

Tie Hua Zhou1, Dongsheng Li1, Zhiwei Jian1

  • 1Department of Computer Science and Technology, School of Computer Science, Northeast Electric Power University, Jilin 132013, China.

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概括

这项研究介绍了一种多式联络系统,用于家庭机器人使用脑电图 (EEG) 和视觉数据来理解老年人的意图. 该系统实现了83.4%的准确性,增强了老年人护理的人机器人协作.

关键词:
功能融合功能融合功能家庭机器人 家庭机器人人与机器人的互动运动成像电脑电图 (MI-EEG)场景识别系统是场景识别系统.

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科学领域:

  • 机器人技术 机器人技术 机器人技术
  • 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:

  • 全球人口老龄化,对老年人的辅助技术需求增加.
  • 家庭机器人提供了日常生活援助的潜力,但需要先进的感知能力.

研究的目的:

  • 为家庭机器人开发一种多式人机交互系统,以感知老年人的意图和环境.
  • 加强老年护理机构中人类和机器人之间的协作互动.

主要方法:

  • 使用运动图像 (MI) EEG信号与频道选择和波器银行共空间模式 (FBCSP) 进行分类.
  • 集成的YOLO v8用于对象检测和机器学习用于场景识别.
  • 将EEG分类与场景识别结合起来,以确定任务识别的场景意图对应.

主要成果:

  • 在意图驱动的任务类型中,实现了83.4%的识别准确度.
  • 通过整合EEG和视觉数据,证明了有效的多式联络感知.
  • 验证了人机协作互动中的实际应用价值.

结论:

  • 拟议的系统提供了一种强大的方法,用于在家庭环境中识别老年人的意图.
  • 这项技术支持开发更智能,个性化的家庭辅助机器人.
  • 这些发现突显了多式联络感知在高级人机交互中对老年护理的潜力.