Rasool Ghulam, Iqbal Kamran, Bouaynaya Nidhal, White Gannon
IEEE Trans Neural Syst Rehabil Eng. 2016 Jan;24(1):98-108. doi: 10.1109/TNSRE.2015.2410176. Epub 2015 Mar 5.
We present a novel formulation that employs task-specific muscle synergies and state-space representation of neural signals to tackle the challenging myoelectric control problem for lower arm prostheses. The proposed framework incorporates information about muscle configurations, e.g., muscles acting synergistically or in agonist/antagonist pairs, using the hypothesis of muscle synergies. The synergy activation coefficients are modeled as the latent system state and are estimated using a constrained Kalman filter. These task-dependent synergy activation coefficients are estimated in real-time from the electromyogram (EMG) data and are used to discriminate between various tasks. The task discrimination is helped by a post-processing algorithm that uses posterior probabilities. The proposed algorithm is robust as well as computationally efficient, yielding a decision with > 90% discrimination accuracy in approximately 3 ms . The real-time performance and controllability of the algorithm were evaluated using the targeted achievement control (TAC) test. The proposed algorithm outperformed common machine learning algorithms for single- as well as multi-degree-of-freedom (DOF) tasks in both off-line discrimination accuracy and real-time controllability (p < 0.01).
我们提出了一种新颖的方法，该方法采用特定任务的肌肉协同作用和神经信号的状态空间表示来解决具有挑战性的前臂假肢肌电控制问题。所提出的框架利用肌肉协同作用的假设，纳入了有关肌肉配置的信息，例如协同作用的肌肉或作为 agonist/antagonist 对的肌肉。协同激活系数被建模为潜在系统状态，并使用约束卡尔曼滤波器进行估计。这些依赖于任务的协同激活系数是从肌电图（EMG）数据实时估计的，并用于区分各种任务。后处理算法使用后验概率，有助于任务区分。所提出的算法既稳健又计算高效，在大约 3 毫秒内产生的决策具有 > 90% 的区分准确率。使用目标达成控制（TAC）测试评估了该算法的实时性能和可控性。在离线区分准确率和实时可控性方面，所提出的算法在单自由度和多自由度（DOF）任务中均优于常见的机器学习算法（p < 0.01）。
