个性化基于学习的地面反应力估计在人们中风后使用压力内
概括
使用压力内的机器学习准确地估计了中风幸存者的3D地面反应力 (GRF). 这项技术可以改善移动障碍患者的步态分析和康复.
科学领域:
- 临床生物力学 临床生物力学
- 康复工程 康复工程
- 可穿戴式传感器技术
背景情况:
- 脑卒中是导致步行障碍的主要原因,影响独立性和生活质量.
- 生物力学分析工具与临床实践之间存在差距,特别是在步态评估方面.
- 传统的3D地面反应力 (3D GRF) 测量需要实验室设备,如强力板.
研究的目的:
- 评估特定主题的机器学习方法,以估计使用压力内对中风后个体的3DGRF.
- 评估这些方法在异质人群中不同步行速度的性能.
- 确定估计的GRF的准确性及其与临床步态指标的相关性.
主要方法:
- 使用压力内收集从中风后的个人在步行过程中收集数据.
- 应用了三个特定主题的机器学习模型来估计3D GRF.
- 验证了估计的GRF与来自强力板的地面真实测量.
- 分析了中侧,前后和垂直GRF组件的估计错误和相关性.
主要成果:
- 基于卷积神经网络 (CNN) 的方法显示了所有GRF组件的最低估计误差.
- 估计的GRF组件显示与地面真相测量有很强的相关性.
- 在估计临床相关的肢点指标时,获得了高准确度.
结论:
- 特定对象的机器学习模型,特别是CNN,显示了准确的3DGRF估计的希望,使用可穿戴的压力内在中风后的步行.
- 这种方法有可能弥合基于实验室的生物力学和中风康复的现实世界的临床应用之间的差距.
- 个性化机器学习为中风幸存者提供了个性化的步态分析和改进的康复策略的途径.
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