Mechtenberg Malte, Schneider Axel
Biomechatronics and Embedded Systems Group, Bielefeld University of Applied Sciences and Arts, Bielefeld, Germany.
Front Neurorobot. 2023 Jul 6;17:1179224. doi: 10.3389/fnbot.2023.1179224. eCollection 2023.
Motion predictions for limbs can be performed using commonly called Hill-based muscle models. For this type of models, a surface electromyogram (sEMG) of the muscle serves as an input signal for the activation of the muscle model. However, the Hill model needs additional information about the mechanical system state of the muscle (current length, velocity, etc.) for a reliable prediction of the muscle force generation and, hence, the prediction of the joint motion. One feature that contains potential information about the state of the muscle is the position of the center of the innervation zone. This feature can be further extracted from the sEMG. To find the center, a wavelet-based algorithm is proposed that localizes motor unit potentials in the individual channels of a single-column sEMG array and then identifies innervation point candidates. In the final step, these innervation point candidates are clustered in a density-based manner. The center of the largest cluster is the estimated center of the innervation zone. The algorithm has been tested in a simulation. For this purpose, an sEMG simulator was developed and implemented that can compute large motor units (1,000's of muscle fibers) quickly (within seconds on a standard PC).
肢体的运动预测可以使用通常所说的基于希尔的肌肉模型来进行。对于这类模型，肌肉的表面肌电图（sEMG）用作肌肉模型激活的输入信号。然而，希尔模型需要关于肌肉机械系统状态（当前长度、速度等）的额外信息，以便可靠地预测肌肉力的产生，进而预测关节运动。一个包含肌肉状态潜在信息的特征是神经支配区中心的位置。这个特征可以从sEMG中进一步提取。为了找到中心，提出了一种基于小波的算法，该算法在单列sEMG阵列的各个通道中定位运动单位电位，然后识别神经支配点候选。在最后一步，这些神经支配点候选以基于密度的方式聚类。最大聚类的中心就是神经支配区的估计中心。该算法已在模拟中进行了测试。为此，开发并实现了一个sEMG模拟器，它可以快速（在标准PC上几秒钟内）计算大型运动单位（数千条肌纤维）。
