一个多分辨率的物理信息循环神经网络:制定和应用到肌肉骨系统.
Karan Taneja1, Xiaolong He2, QiZhi He3
1Department of Structural Engineering, University of California San Diego, La Jolla, CA USA.
Computational mechanics
|May 3, 2024
概括
这项研究引入了一种新的多分辨率物理信息循环神经网络 (MR PI-RNN),用于预测肌肉骨运动,并从表面肌电图 (sEMG) 信号中识别系统参数.
科学领域:
- 生物力学 生物力学
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 从表面电肌图 (sEMG) 预测肌肉骨 (MSK) 运动是复杂的,因为不同的信号频率.
- 准确的映射需要先进的计算模型来捕捉肌肉动态和关节运动.
研究的目的:
- 开发一个多分辨率的物理信息循环神经网络 (MR PI-RNN),用于同时进行MSK运动预测和参数识别.
- 为了应对将高频sEMG信号映射到低频关节运动的挑战.
主要方法:
- 利用快速波段转换将sEMG和联合运动信号分解为多分辨率组件.
- 使用一个封闭的循环单元 (GRU) 进行粗度信号的训练,参数被递归转移到更细的尺度 (转移学习).
- 保证的训练满足了基础动态平衡,用于基于物理学的预测.
主要成果:
- 与单个尺度训练相比,MR PI-RNN框架在预测肘部曲延伸运动方面表现出更高的准确性.
- 从受试者的动力学数据中成功确定了生理上一致的肌肉参数.
- 创建了一个以物理为基础的前进动力学替代模型.
结论:
- 拟议的MR PI-RNN框架有效地整合了多分辨率分析和基于物理的深度学习,用于MSK运动预测.
- 这种方法提高了准确性,并使复杂的生物机械系统可靠的参数识别.
- 为理解和建模神经肌肉控制和MSK动态提供了强大的方法.
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