Neural drive estimation using the hypothesis of muscle synergies and the state-constrained Kalman filter

G. Rasool, K. Iqbal, N. Bouaynaya, G. White
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引用次数: 11

Abstract

We explore the hypothesis of muscle synergies to estimate the neural drive (movement intent) for upper extremity myoelectric prosthesis using the surface myoelectric signals. Commonly employed pattern classification systems have certain limitations, like inherent discrete nature, finite movement classes and limited degrees-of-freedom. We propose a novel framework based on the state space modeling and the hypothesis of muscle synergies. The problem is formulated in the state space framework in a novel way, where the movement intent is modeled as the hidden state of the system. A continuous stream of the movement intent (the hidden state) is estimated using the state-constrained Kalman filter. Preliminary experimental results also confirm the applicability of the proposed framework for estimation of movement intent.
基于肌肉协同假设和状态约束卡尔曼滤波的神经驱动估计
我们利用肌电表面信号,探讨了肌肉协同作用的假设,以估计上肢肌电假体的神经驱动(运动意图)。常用的模式分类系统具有一定的局限性,如固有的离散性、有限的运动类和有限的自由度。我们提出了一个基于状态空间建模和肌肉协同假设的新框架。该问题在状态空间框架中以一种新颖的方式表述,将运动意图建模为系统的隐藏状态。使用状态约束卡尔曼滤波器估计运动意图的连续流(隐藏状态)。初步的实验结果也证实了所提出的框架对运动意图估计的适用性。
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