认知障碍的端到端分类使用日常生活步态数据
概括
商业智能设备显示,使用步态分析预先选阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 等认知障碍具有前景. 可穿戴传感器检测到步态变化,帮助那些需要治疗的人进行早期干预.
科学领域:
- 数字健康数字健康
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
背景情况:
- 认知障碍,包括轻度阿尔茨海默病 (AD),轻度认知障碍 (MCI) 和主观认知衰退 (SCD),需要早期检测及时干预.
- 使用现实世界的数据来区分患有MCI或AD的人与患有SCD的人,由于症状重叠和不受控制的环境,这 presents presents是一个重大挑战.
研究的目的:
- 研究使用商业智能设备用于预先选认知障碍的可行性.
- 根据日常生活中收集的与步态相关的数据,区分需要治疗 (AD和MCI) 的人与没有认知障碍 (SCD) 的人.
主要方法:
- 招募了125名50岁以上的个人,并根据认知诊断进行了分类.
- 参与者使用智能手机和智能手表一个月,在日常活动中收集加速度计和陀螺仪数据.
- 在预处理的行走段上训练了一个深度学习分类器,以区分认知障碍组.
主要成果:
- 深度学习模型实现了认知障碍检测曲线下的面积 (AUC) 0.70.
- 一项传感器除研究表明,单独使用手腕陀螺仪的数据就产生了0.70.7的可比AUC.
- 整合智能手机加速度计和智能手表陀螺仪提高了分类性能.
结论:
- 商业智能设备和基于可穿戴的步态分析显示了预先选认知障碍的潜力.
- 使用可访问技术进行步行分析可以帮助早期检测AD和MCI等疾病.
- 未来的研究将集中在多式联网数据融合上,以提高认知障碍查工具的准确性和可靠性.
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