State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai, People's Republic of China.
J Neural Eng. 2022 Feb 17;19(1). doi: 10.1088/1741-2552/ac4851.
Musculoskeletal model (MM) driven by electromyography (EMG) signals has been identified as a promising approach to predicting human motions in the control of prostheses and robots. However, muscle excitations in MMs are generally derived from the EMG signals of the targeted sensor covering the muscle, inconsistent with the fact that signals of a sensor are from multiple muscles considering signal crosstalk in actual situation. To identify more accurate muscle excitations for MM in the presence of crosstalk, we proposed a novel excitation-extracting method inspired by muscle synergy for simultaneously estimating hand and wrist movements.Muscle excitations were firstly extracted using a two-step muscle synergy-derived method. Specifically, we calculated subject-specific muscle weighting matrix and corresponding profiles according to contributions of different muscles for movements derived from synergistic motion relation. Then, the improved excitations were used to simultaneously estimate hand and wrist movements through musculoskeletal modeling. Moreover, the offline comparison among the proposed method, traditional MM and regression methods, and an online test of the proposed method were conducted.The offline experiments demonstrated that the proposed approach outperformed the EMG envelope-driven MM and three regression models with higher R and lower NRMSE. Furthermore, the comparison of excitations of two MMs validated the effectiveness of the proposed approach in extracting muscle excitations in the presence of crosstalk. The online test further indicated the superior performance of the proposed method than the MM driven by EMG envelopes.The proposed excitation-extracting method identified more accurate neural commands for MMs, providing a promising approach in rehabilitation and robot control to model the transformation from surface EMG to joint kinematics.
基于肌电图 (EMG) 信号的肌肉骨骼模型 (MM) 已被确定为一种很有前途的方法，可以预测假肢和机器人控制中的人体运动。然而，MM 中的肌肉激发通常是从覆盖肌肉的目标传感器的 EMG 信号中得出的，与实际情况下信号串扰导致传感器信号来自多个肌肉的事实不一致。为了在存在串扰的情况下为 MM 识别更准确的肌肉激发，我们提出了一种受肌肉协同作用启发的新的激发提取方法，用于同时估计手和手腕运动。
首先使用两步肌协同衍生方法提取肌肉激发。具体来说，我们根据协同运动关系衍生的运动中不同肌肉的贡献，计算出特定于主题的肌肉加权矩阵和相应的轮廓。然后，通过肌肉骨骼建模使用改进的激发来同时估计手和手腕运动。此外，还进行了所提出方法、传统 MM 和回归方法之间的离线比较以及所提出方法的在线测试。
离线实验表明，与传统的基于肌电包络的 MM 和三种回归模型相比，所提出的方法具有更高的 R 和更低的 NRMSE，因此表现更好。此外，对两种 MM 的激发的比较验证了所提出的方法在存在串扰的情况下提取肌肉激发的有效性。在线测试进一步表明，与基于肌电包络的 MM 相比，所提出的方法具有更好的性能。
所提出的激发提取方法为 MM 识别了更准确的神经命令，为表面肌电到关节运动学的转换建模提供了一种有前途的康复和机器人控制方法。
