基于机器学习方法的步行阶段的分类,使用肌肉协同作用
Heesu Park1,2, Sungmin Han1,3, Joohwan Sung1
1Biomedical Research Division, Korea Institute of Science and Technology, Seoul, Republic of Korea.
Frontiers in human neuroscience
|June 2, 2023
概括
肌肉协同功能提供比传统方法更准确的步态阶段分类. 这种方法增强了用于诊断障碍和控制辅助设备的步行事件的检测.
科学领域:
- 生物力学 生物力学
- 神经科学是一个神经科学.
- 机器学习 机器学习
背景情况:
- 准确的步行阶段检测对于诊断神经/肌肉骨疾病和控制辅助设备至关重要.
- 肌肉协同激活模式,称为肌肉协同作用,对于理解运动和康复是有用的.
研究的目的:
- 调查肌肉协同功能是否与传统的时间域和波形特征相比,提供更准确和更强大的步态事件分类.
- 分析步行过程中人类运动系统的功能组织.
主要方法:
- 电肌图 (EMG) 信号从八名健康人群中收集到.
- 非负矩阵因子化 (NNMF) 确定了肌肉协同作用.
- 使用决策树,KNN,SVM和神经网络算法对行走阶段进行了分类.
主要成果:
- 与传统的EMG特征相比,肌肉协同功能的特征证明了步行阶段的优越分类准确性.
- 这些发现支持了肌肉协同作用能够准确地分类步态阶段的假设.
结论:
- 肌肉协同分析为步态分析提供了一种新且有效的方法.
- 肌肉协同作用显示出作为临床和辅助设备应用中准确的步行阶段检测工具的巨大潜力.
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