用可穿戴惯性传感器和监督和无监督机器学习来识别中风患者的关键步态特征
Paolo Brasiliano1,2, Amaranta Soledad Orejel-Bustos3,4, Valeria Belluscio3
1Department of Motor, Human and Health Sciences, University of Rome "Foro Italico", Rome, 00135, Italy. paolobrasilianores@gmail.com.
Scientific reports
|March 9, 2026
概括
使用可穿戴传感器的机器学习准确地识别了中风步行障碍. 关键特征包括步伐速度,步态变化,干/头部运动流,有助于康复规划.
科学领域:
- 生物力学 生物力学
- 康复工程 康复工程 康复工程
- 机器学习 机器学习
背景情况:
- 脑卒中是导致运动残疾的主要原因,严重影响行走能力和生活质量.
- 使用磁性惯性测量单位 (MIMU) 的可穿戴步态分析为量化中风后运动损伤提供了一种有前途的方法.
研究的目的:
- 采用机器学习来识别步态特征,从而有效地区分中风患者和健康人群.
- 通过结合监督和无监督机器学习方法,提高步态分析的可解释性和通用性.
主要方法:
- 八十五名中风患者和97名健康对照者在佩戴五个MIMU时进行了10米步行测试.
- 监督的顺序向后特征选择确定了关键步行指标 (时空,对称性,稳定性,平滑性).
- 机器学习分类器 (KNN,SVM,TREE) 和无监督的k-medoids集群被用于验证特征区分能力.
主要成果:
- 九个选定的特征实现了高分类精度:94.1% (KNN),96.7% (SVM) 和89.1% (TREE).
- 支持矢量机 (SVM) 显示出最好的泛化性能.
- 无监督集群识别了三个核心特征 (步速,立场相变系数,中侧波比) 的准确率为90%.
结论:
- 步态的变化,干的光滑和上半身的稳定性是中风后运动功能障碍的强有力的指标.
- 头部运动的平滑性被确定为中风患者的一个新的,有区别的特征.
- 这个框架整合了可穿戴传感器和机器学习,以推进临床步态分析,并为康复策略提供信息.
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