基于关节扭矩的sEMG计算模型的研究
Xu Qiu1, Haiming Zhao1,2, Peng Xu1
1College of Mechanical and Electrical Engineering, Central South University, Changsha 410083, China.
Sensors (Basel, Switzerland)
|May 11, 2024
概括
这项研究使用表面电肌图 (sEMG) 和希尔模型开发了一种用于脚关节运动的预测模型. 优化的模型准确地预测了脚扭矩,有助于开发有效的脚康复设备.
科学领域:
- 生物力学 生物力学
- 康复工程 康复工程 康复工程
- 生物医学信号处理
背景情况:
- 足关节运动预测对于开发先进的康复设备至关重要.
- 表面电肌图 (sEMG) 为了解关节运动期间的肌肉活动提供了有价值的数据.
- 希尔模型为分析肌肉诱导的关节扭矩提供了一个框架.
研究的目的:
- 建立足关节运动的预测模型.
- 分析sEMG对脚关节扭矩预测的影响.
- 为脚康复控制系统提供理论基础.
主要方法:
- 利用表面电肌图 (sEMG) 数据和希尔模型来计算脚关节的扭矩.
- 收集和分析了9名受试者在各种脚运动期间的sEMG,关节角度和运动信号.
- 采用遗传算法来优化希尔模型中的16个生理参数.
主要成果:
- 优化的Hill模型精确计算了脚关节的扭矩,与测量扭矩相比,根平均平方误差低于1.4.
- 该模型在预测扭矩模式时表现出精确度,在背部屈曲/屈曲,Varus和内部/外部旋转期间.
- 用实验数据和遗传算法确定和优化了生理参数.
结论:
- 开发的脚运动预测模型为脚康复控制提供了坚实的理论基础.
- 精确的扭矩预测提高了用于脚康复的机器人系统的有效性.
- 这些发现支持对脚损伤的智能康复设备的发展.
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