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在智能家居中进行不显眼的认知评估:利用视觉编码和合成运动痕迹数据挖掘.

Samaneh Zolfaghari1, Annica Kristoffersson1, Mia Folke1

  • 1School of Innovation, Design and Engineering, Division of Intelligent Future Technologies, Mälardalen University, 721 23 Västerås, Sweden.

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概括

智能家居传感器检测室内异常运动模式,以识别老年人认知能力下降. 这种创新方法准确地区分了认知健康的个人和痴呆症患者.

关键词:
环境辅助生活环境辅助生活环境传感器传感器环境传感器环境传感器机器学习是机器学习.智能环境 智能环境运行轨迹 采矿 的 轨迹视觉特征提取 视觉特征提取

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科学领域:

  • 老年学是指老年学的学科.
  • 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:

  • 智能家居传感器为监控老年人提供了非侵入性的方法.
  • 运动痕迹越来越多地用于检测认知障碍的早期迹象.

研究的目的:

  • 利用智能家居传感器数据开发一种用于识别老年人认知衰退的创新系统.
  • 分析室内运动模式,以便早期检测认知障碍.

主要方法:

  • 使用非侵入性智能家居传感器 (PIR,嵌入物体) 来收集运动数据.
  • 在地图上可视化用户运动痕迹和对象交互.
  • 采用图像描述器特征和合成少数群体过量采样技术进行分析.

主要成果:

  • 一个功能原型系统在99名老年人的数据集上进行了测试.
  • 该系统在区分认知健康个体和痴呆症患者时获得了72.22%的宏观平均F1分数.
  • 与现有最先进的方法相比,表现出优越的性能.

结论:

  • 拟议的系统有效地通过异常的室内运动模式来识别老年人的认知状态.
  • 智能家居传感器数据集成为认知健康评估提供了灵活有效的方法.
  • 这项技术支持独立生活和对认知衰退的早期干预.