使用物理信息和域适应神经网络从表面EMG估计IMU信号
17271 E Sonoran Arroyo Mall, Mesa, AZ 85212, United States; The Polytechnic School, Ira A. Schools of Engineering, Arizona State University, Mesa, AZ, United States.
概括
这项研究引入了一种新的基于物理的神经网络,用于估计不同任务中的EMG信号的身体运动. 该模型准确地预测了无需重新训练的运动,从而推进了生物力学分析.
科学领域:
- 生物医学工程 生物医学工程
- 机器学习 机器学习
- 生物力学 生物力学
背景情况:
- 从表面电肌图 (EMG) 信号中估计人体动力学是具有挑战性的,因为肌肉活动和运动之间的复杂,非线性关系.
- 现有的方法通常需要针对不同任务进行特定领域的调整或再培训,从而限制了它们的通用性.
研究的目的:
- 开发一个基于物理的,域适应的神经网络架构,用于在异质的人类任务领域中从EMG信号中进行强大的身体动力学估计.
- 通过直接预测来自EMG信号的惯性测量单元 (IMU) 输出而解决生物力学反向映射问题,而无需针对特定任务的调整.
主要方法:
- 一个共享的卷积特征提取器,其后有两个分支:用于IMU信号预测的回归头和使用梯度反转层的对抗域分类头.
- 整合了基于物理学的损失术语,对预测加速 (冲击) 的导数进行惩罚,以确保生物力学可信性.
- 使用EMG-IMU数据对五个不同的任务类别进行评估,具有各种传感器配置.
主要成果:
- 在57个IMU通道中,实现了0.42±0.11m/s2的加速和6.38±1.1°/s的陀螺仪输出的根平均平方误差.
- 该模型显示域预测准确度接近75%,表明任务不变表示的有效学习.
- 性能指标根据域的复杂性和EMG/运动模式的重叠而有所不同.
结论:
- 拟议的基于物理的,域适应的神经网络有效地估计了来自EMG信号的身体动力学,在各种任务中无需重新训练.
- 该架构学习了可概括的表示,同时强制执行生物力学约束,为运动分析提供了有前途的方法.
- 这种方法提升了各种应用中非侵入性,准确的运动跟踪的潜力.
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