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Updated: Jul 19, 2025

Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability

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Published on: September 18, 2020

机械肌学信号模式识别膝盖和脚运动使用集群智能基于算法的特征选择方法.

Yue Zhang¹, Maoxun Sun², Chunming Xia³

¹School of Mechanical Engineering, Nantong University, Nantong 226019, China.

Sensors (Basel, Switzerland)

|August 12, 2023

概括

本研究介绍了驼群算法 (CSA) 和虫优化算法 (GOA),用于识别可穿戴设备中使用机械图 (MMG) 信号的下肢运动. CSA实现了更高的准确性,而GOA则更快,功能更少.

关键词:

黑猩猩群算法黑猩猩群算法功能选择功能选择虫优化算法虫优化算法机械学图 (mechanomyography) 是一种机械学图.模式识别模式识别模式识别

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科学领域:

生物医学工程生物医学工程
康复技术康复技术康复技术
信号处理信号处理

背景情况:

机械肌图 (MMG) 信号模式识别对于开发有效的可穿戴康复训练设备至关重要.
准确识别下肢运动,如膝盖和脚动作,对于个性化康复至关重要.
现有的方法可能缺乏复杂的运动模式的效率或最佳特征选择.

研究的目的:

提出和评估使用驼群算法 (CSA) 和虫优化算法 (GOA) 的新型MMG特征选择方法.
评估CSA和GOA在识别坐着和站着的膝盖和脚运动中的表现.
为了比较两种算法的分类准确性,特征选择和计算时间之间的权衡.

主要方法:

设计并使用无线多通道MMG采集系统来收集来自大腿肌肉部位的信号.
实施CSA和GOA用于从MMG数据中选择特征.
分析了不同值对分类准确性的影响.
坐着和站着的位置的评估认可率.

主要成果:

在消除冗余信息后,CSA和GOA都实现了高的认可率.
随着门的增加,CSA表现强,识别率波动至88.17% (坐着) 和90.07% (站着).
阿尔巴尼亚政府的识别率随着门的增加而大幅下降,但消耗的时间更少,选择的功能更少.

结论:

与印度政府相比,CSA为膝盖和脚运动提供了更高的识别率.
印度政府提供了一个更有效的功能选择过程,具有更少的功能集.
在CSA和GOA之间做出选择取决于基于MMG的康复系统对准确性和效率的具体要求.