向负载-坚固的运动估计使用EMG驱动的状态空间模型与可变刚性的肌肉骨模型
概括
这项研究引入了一种新的电肌图 (EMG) 模型,用于准确估计人类运动. 该模型可以提高神经机器接口在不同重量负载的性能,而无需重新训练.
科学领域:
- 生物医学工程 生物医学工程
- 神经科学是一个神经科学.
- 康复工程 康复工程 康复工程
背景情况:
- 精确的人类运动估计对于电肌图 (EMG) 信号驱动的神经机器接口 (NMI) 至关重要.
- 不同的外部负荷显著影响基于EMG的NMI的性能,通常是由于肌肉协同收缩的未解决的变化.
- 现有的EMG驱动的肌肉骨模型 (MMs) 在不同的负载条件下显示出强度的限制.
研究的目的:
- 开发一个强大的EMG驱动的状态空间模型来估计手和手腕的运动.
- 在不同负载条件下提高NMI的性能,而不需要再培训.
- 为了应对影响基于EMG的运动估计的肌肉合收缩的负载诱导变化的挑战.
主要方法:
- 提出了一个EMG驱动的状态空间模型,将肌肉骨模型 (MM) 与可变关节刚度作为状态模型.
- 利用反向传播的神经网络作为观察模型,将状态变量映射到EMG特征中.
- 仅在零负载数据上训练模型,并在四个不同的负载条件下验证其性能.
主要成果:
- 在零负载数据上训练的拟议模型实现了与在负载特定数据上训练的传统MM相比的性能.
- 显著优于传统的MMs,这些MMs仅在不同负载的零负载数据上进行训练.
- 在不同的负载条件下证明了基于EMG的接口的强度和精度的提高.
结论:
- 开发的EMG驱动状态空间模型有效地提高了NMI人类运动估计的稳定性和准确性.
- 该方法为NMI在日常活动中使用可变外部负载的NMI提供了有前途的解决方案.
- 验证了状态空间建模和神经网络的潜力,用于基于EMG的自适应运动跟踪.
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