多模式肌肉激活建模使用库普曼操作员线性化为关节外骨
概括
这项研究引入了一种新的数据驱动模式,用于步行康复. 它通过使用融合肌肉信号精确估计脚扭矩来增强人机交互,以更好地协助外骨架.
科学领域:
- 生物力学 生物力学
- 机器人技术 机器人技术 机器人技术
- 康复工程 康复工程
背景情况:
- 步行障碍显著降低了流动性和生活质量.
- 智能康复设备需要动态模型来实现有效的人机交互和协助.
- 准确估计意志扭矩对于个性化步行支至关重要.
研究的目的:
- 开发一个数据驱动的模型,用于估计步行周期期间的自愿脚扭矩.
- 将Koopman操作员的线性估计与化表面电肌学 (sEMG) 和超声波 (US) 成像相结合.
- 为了提高状态和意志扭矩估计的准确性,以加强步态康复.
主要方法:
- 利用库普曼操作员的线性估计来建模非线性,相位依赖的脚运动动态.
- 融合表面电肌图 (sEMG) 和超声波 (US) 成像数据,以增强肌肉激活预测.
- 开发了一种数据驱动的框架,用于在整个步行周期中实时预定扭矩估计.
主要成果:
- 综合模型在估计 volitional 脚扭矩方面表现出更高的准确性.
- sEMG和US信号的融合为肌肉激活提供了结合的电气和形态洞察力.
- 该框架成功估计了脚运动的非线性,相位依赖的动态.
结论:
- 拟议的数据驱动模型显示了改善步行康复策略的巨大潜力.
- 通过融合sEMG,美国成像和库普曼操作员方法,可以实现准确的意志扭矩估计.
- 这一框架为个性化外骨架辅助和更有效的步行康复奠定了基础.
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