使用助听器集成加速度计估计步骤长度和步行速度:不同算法的比较
IEEE journal of biomedical and health informatics
|September 5, 2024
概括
耳戴式传感器可以准确估计步行速度和步伐长度,这对于评估健康和跌倒风险至关重要. 这种新的机器学习方法可以在日常生活中持续监控步态.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 数字健康数字健康
背景情况:
- 步态分析对于健康监测至关重要,因为与跌倒和认知衰退相关的异常.
- 可穿戴式传感器,特别是不引人注目的可穿戴式传感器,可以在家中持续评估步态.
- 目前还缺乏使用耳戴式加速度计进行步态分析的标准化管道.
研究的目的:
- 开发和比较算法来估计时空步行参数 (步长,步行速度) 使用耳戴式加速度计.
- 为耳戴传感器建立一个全面的步态分析管道.
- 通过光学运动捕捉来验证这些估计的精度和临床相关性.
主要方法:
- 开发了三种算法方法:生物机械模型,基于特征的机器学习 (ML) 和卷积神经网络.
- 性能被评估在步骤和步行比赛水平上,将估计值与光学运动捕获进行比较.
- 这些模型利用了耳朵佩戴加速度计的数据.
主要成果:
- 基于特征的ML模型表现出卓越的性能,在步行比赛水平上达到4.8厘米的步骤长度精度.
- 使用ML方法估计的步行速度的绝对百分比误差为5.4% (±4.0%).
- ML模型显示了跨年龄组和采样率的稳定性,尽管对步行速度变化敏感.
结论:
- 机器学习模型可以准确地估计步数长度和步行速度从耳戴式加速度计与临床相关的精度.
- 这项研究开创了使用耳戴式传感器的步态分析,使持续的长期健康监测成为可能.
- 开发的框架支持平静的数字健康评估,并将其整合到日常工作中.
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