Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 23900 Lecco, Italy.
Institute of Intelligent Industrial Systems and Technologies for Advanced Manufacturing (STIIMA), Italian Council of National Research (CNR), 20133 Milan, Italy.
Sensors (Basel). 2022 Nov 8;22(22):8584. doi: 10.3390/s22228584.
The muscle synergy approach is used to evaluate motor control and to quantitatively determine the number and structure of the modules underlying movement. In experimental studies regarding the upper limb, typically 8 to 16 EMG probes are used depending on the application, although the number of muscles involved in motor generation is higher. Therefore, the number of motor modules may be underestimated and the structure altered with the standard spatial synergy model based on the non-negative matrix factorization (NMF). In this study, we compared the number and structure of muscle synergies when considering 12 muscles (an "average" condition that represents previous studies) and 32 muscles of the upper limb, also including multiple muscle heads and deep muscles. First, we estimated the muscle activations with an upper-limb model in OpenSim using data from multi-directional reaching movements acquired in experimental sessions; then, spatial synergies were extracted from EMG activations from 12 muscles and from 32 muscles and their structures were compared. Finally, we compared muscle synergies obtained from OpenSim and from real experimental EMG signals to assess the reliability of the results. Interestingly, we found that on average, an additional synergy is needed to reconstruct the same level with 32 muscles with respect to 12 muscles; synergies have a very similar structure, although muscles with comparable physiological functions were added to the synergies extracted with 12 muscles. The additional synergies, instead, captured patterns that could not be identified with only 12 muscles. We concluded that current studies may slightly underestimate the number of controlled synergies, even though the main structure of synergies is not modified when adding more muscles. We also show that EMG activations estimated with OpenSim are in partial (but not complete) agreement with experimental recordings. These findings may have significative implications for motor control and clinical studies.
肌肉协同作用方法用于评估运动控制,并定量确定运动的模块数量和结构。在上肢的实验研究中,通常根据应用情况使用 8 到 16 个肌电图探头,尽管参与运动产生的肌肉数量更多。因此,基于非负矩阵分解(NMF)的标准空间协同模型可能会低估运动模块的数量并改变其结构。在这项研究中,我们比较了考虑 12 块肌肉(代表先前研究的“平均”情况)和 32 块上肢肌肉时的肌肉协同作用数量和结构,还包括多个肌肉头和深部肌肉。首先,我们使用 OpenSim 中的上肢模型根据实验会话中获取的多向伸展运动数据来估计肌肉激活;然后,从 12 块肌肉和 32 块肌肉的肌电图激活中提取空间协同作用,并比较它们的结构。最后,我们比较了从 OpenSim 和真实实验肌电图信号中获得的肌肉协同作用,以评估结果的可靠性。有趣的是,我们发现,平均而言,与 12 块肌肉相比,需要额外的协同作用来重建相同的水平;尽管添加了具有可比生理功能的肌肉,但协同作用具有非常相似的结构。相反,额外的协同作用捕捉到了仅用 12 块肌肉无法识别的模式。我们得出的结论是,即使添加更多肌肉不会改变协同作用的主要结构,当前的研究可能会略微低估受控制的协同作用的数量。我们还表明,OpenSim 估计的肌电图激活与实验记录部分(但不完全)一致。这些发现可能对运动控制和临床研究具有重要意义。