使用惯性测量单位检测动态稳定性变化在多条件步态实验
Yasuhirio Akiyama1, Kyogo Kazumura2, Shogo Okamoto3
1Faculty of Textile Science and Technology, Shinshu University, Nagano 386-8567, Japan.
Sensors (Basel, Switzerland)
|November 9, 2024
概括
本研究介绍了一种可穿戴系统,使用惯性测量单位 (IMU) 来估计步行稳定性 (稳定性边际). 卷积神经网络达到90%以上的准确性,为日常步行评估提供了一个有前途的工具.
科学领域:
- 生物力学 生物力学
- 可穿戴技术可穿戴技术
- 机器学习 机器学习
背景情况:
- 步态评估对于评估移动性和稳定性至关重要.
- 目前的方法通常需要控制的实验室设置.
- 可穿戴式传感器为不引人注目的,现实世界的步态分析提供了潜力.
研究的目的:
- 开发和验证使用惯性测量单位 (IMU) 的可穿戴步态评估方法.
- 通过使用卷积神经网络 (CNN) 来估计来自IMU加速数据的稳定性边际 (MoS).
- 在各种条件下调查MoS与其他步行稳定性指数之间的关系.
主要方法:
- 利用放置在不同身体部位的五个IMU收集加速时间序列数据.
- 开发了一个CNN模型,从IMU数据估计MoS.
- 通过改变跑步机速度和诱导与膝盖-脚-脚形状的不对称性来模拟不同的步态条件.
- 检查了MoS,利亚普诺夫指数和多站点时间序列 (MSTS) 指数之间的相关性.
主要成果:
- 美国有线电视新闻网 (CNN) 模型在向前和向侧方向分类MoS时达到90%以上的准确性.
- 在MoS和利亚普诺夫指数或MSTS指数之间观察到较弱的相关性.
- 研究结果表明,这些指数可能反映了步态稳定的不同方面.
结论:
- 使用CNN的基于IMU的可穿戴方法可以准确地估计MoS用于步态评估.
- 这种方法显示出在日常环境中评估步行稳定的潜力.
- 需要进一步的研究来理解不同稳定性指数提供的互补信息.
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