Finger Force Estimation Using Motor Unit Discharges Across Forearm Postures
Finger Force Estimation Using Motor Unit Discharges Across Forearm Postures
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DOI：
10.1109/tbme.2022.3153448
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发表时间：
2022-02
影响因子：
4.6
通讯作者：
N. Rubin;Yang Zheng;H. Huang;Xiaogang Hu
中科院分区：
文献类型：
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作者：
N. Rubin;Yang Zheng;H. Huang;Xiaogang Hu
背景：基于肌电的解码在上肢神经机器接口中已经得到普及。从表面肌电图（EMG）信号分解的运动单元（MU）放电可以表示运动意图，但是由于电极移位和不同的神经肌肉状态，不同手臂配置的EMG属性可以改变。这项研究调查是否等距指尖力估计使用MU发射是强大的前臂旋转从中立的完全旋前或旋后姿势。研究方法：我们以两种方式从趾总伸肌的高密度EMG中提取MU信息：（1）在所有三个姿势中分解EMG（MU-AllPost）;以及（2）在中性姿势中分解EMG（MU-Neu），并且提取的MU（分离矩阵）被应用于其他姿势。使用回归分析将群体MU击发频率估计的力缩放到受试者的最大随意收缩（MVC）。并与传统的肌电幅值法进行了比较。结果如下：我们发现两种MU方法的均方根误差（RMSE）基本相似，表明MU分解对姿势差异具有鲁棒性。MU方法在无名指（EMG = 6.23，MU-AllPost = 5.72，MU-Neu = 5.64%MVC）和小指（EMG = 6.12，MU-AllPost = 4.95，MU-Neu = 5.36%MVC）中表现出较低的RMSE，中指（EMG = 5.47，MU-AllPost = 5.52，MU-Neu = 6.19%MVC）中具有混合结果。结论：我们的研究结果表明，MU发射可以可靠地提取前臂姿势的影响很小，突出了其作为辅助设备的鲁棒性和连续控制的替代解码方案的潜力。
Background: Myoelectric- based decoding has gained popularity in upper- limb neural-machine interfaces. Motor unit (MU) firings decomposed from surface electromyographic (EMG) signals can represent motor intent, but EMG properties at different arm configurations can change due to electrode shift and differing neuromuscular states. This study investigated whether isometric fingertip force estimation using MU firings is robust to forearm rotations from a neutral to either a fully pronated or supinated posture. Methods: We extracted MU information from high- density EMG of the extensor digitorum communis in two ways: (1) Decomposed EMG in all three postures (MU-AllPost); and (2) Decomposed EMG in neutral posture (MU-Neu), and extracted MUs (separation matrix) were applied to other postures. Populational MU firing frequency estimated forces scaled to subjects’ maximum voluntary contraction (MVC) using a regression analysis. The results were compared with the conventional EMG-amplitude method. Results: We found largely similar root-mean-square errors (RMSE) for the two MU-methods, indicating that MU decomposition was robust to postural differences. MU-methods demonstrated lower RMSE in the ring (EMG = 6.23, MU-AllPost = 5.72, MU-Neu = 5.64% MVC) and pinky (EMG = 6.12, MU-AllPost = 4.95, MU-Neu = 5.36% MVC) fingers, with mixed results in the middle finger (EMG = 5.47, MU-AllPost = 5.52, MU-Neu = 6.19% MVC). Conclusion: Our results suggest that MU firings can be extracted reliably with little influence from forearm posture, highlighting its potential as an alternative decoding scheme for robust and continuous control of assistive devices.