为个性化的手和手腕肌肉骨建模和运动估计进行层次优化
IEEE transactions on bio-medical engineering
|September 9, 2024
概括
个性化的肌肉骨模型可以提高人机交互的运动估计准确度. 层次优化通过考虑个体生理学来增强表面肌电学 (sEMG) 驱动的模型.
科学领域:
- 生物力学 生物力学
- 人与计算机的交互
- 计算建模 计算建模
背景情况:
- 一般的肌肉骨模型由于个体生理差异而缺乏准确性.
- 表面电肌图 (sEMG) 驱动的模型对人机交互有希望,但需要个性化.
- 准确的运动估计对于有效的人机交互应用程序至关重要.
研究的目的:
- 优化一般的肌肉骨模型成为个性化的模型,以提高运动估计的准确性.
- 为个性化肌肉骨模型 (HOPE-MM) 开发一个层次优化方法,考虑手腕和手的生理合.
- 为了简化和提高效率,确定关键的肌肉骨模型参数.
主要方法:
- 为个性化肌肉骨模型 (HOPE-MM) 开发了一种层次优化方法,灵感来自手腕/手动运动合.
- 个性化模型的有效性通过估计单个和同时的关节运动来验证.
- 进行了Sobol灵敏度分析,以确定关键模型参数.
主要成果:
- 个性化的模型实现了高准确性,平均Pearson相关系数为手腕0.95 ± 0.03,甲手腕关节 (MCP) 关节运动为0.93 ± 0.01.
- 与最先进的方法相比,性能显著提高.
- 优化关键参数,如肌松的长度,最大的同位力和最佳的纤维长度允许简化模型与全参数模型性能匹配.
结论:
- 通过层次优化个性化的肌肉骨模型显著提高了运动估计的准确性.
- 了解肌参数效应指导模型开发和简化.
- 个性化模型对于推进康复和机器人控制的临床应用至关重要.
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