Gopalakrishnan Anantharaman, Modenese Luca, Phillips Andrew T M
The Royal British Legion Centre for Blast Injury Studies at Imperial College London London, UK ; Structural Biomechanics, Department of Civil and Environmental Engineering, Imperial College London London, UK.
Griffith Health Institute, Centre for Musculoskeletal Research, Griffith University Gold Coast, QLD, Australia.
Front Comput Neurosci. 2014 Dec 3;8:153. doi: 10.3389/fncom.2014.00153. eCollection 2014.
Prior experimental studies have hypothesized the existence of a "muscle synergy" based control scheme for producing limb movements and locomotion in vertebrates. Such synergies have been suggested to consist of fixed muscle grouping schemes with the co-activation of all muscles in a synergy resulting in limb movement. Quantitative representations of these groupings (termed muscle weightings) and their control signals (termed synergy controls) have traditionally been derived by the factorization of experimentally measured EMG. This study presents a novel approach for deducing these weightings and controls from inverse dynamic joint moments that are computed from an alternative set of experimental measurements-movement kinematics and kinetics. This technique was applied to joint moments for healthy human walking at 0.7 and 1.7 m/s, and two sets of "simulated" synergies were computed based on two different criteria (1) synergies were required to minimize errors between experimental and simulated joint moments in a musculoskeletal model (pure-synergy solution) (2) along with minimizing joint moment errors, synergies also minimized muscle activation levels (optimal-synergy solution). On comparing the two solutions, it was observed that the introduction of optimality requirements (optimal-synergy) to a control strategy solely aimed at reproducing the joint moments (pure-synergy) did not necessitate major changes in the muscle grouping within synergies or the temporal profiles of synergy control signals. Synergies from both the simulated solutions exhibited many similarities to EMG derived synergies from a previously published study, thus implying that the analysis of the two different types of experimental data reveals similar, underlying synergy structures.
先前的实验研究推测，脊椎动物中存在一种基于“肌肉协同作用”的控制方案来产生肢体运动和 locomotion。有人认为，这种协同作用由固定的肌肉分组方案组成，协同作用中所有肌肉的共同激活会导致肢体运动。这些分组的定量表示（称为肌肉权重）及其控制信号（称为协同控制）传统上是通过对实验测量的肌电图进行分解得出的。本研究提出了一种新方法，可从由另一组实验测量——运动运动学和动力学计算得出的逆动态关节力矩中推导这些权重和控制。该技术应用于健康人以 0.7 和 1.7 m/s 的速度行走时的关节力矩，并基于两种不同标准计算了两组“模拟”协同作用：(1) 要求协同作用最小化肌肉骨骼模型中实验和模拟关节力矩之间的误差（纯协同作用解）；(2) 除了最小化关节力矩误差外，协同作用还最小化肌肉激活水平（最优协同作用解）。在比较这两种解时，观察到将最优性要求（最优协同作用）引入仅旨在再现关节力矩的控制策略（纯协同作用）并不一定需要协同作用内的肌肉分组或协同控制信号的时间分布发生重大变化。两种模拟解的协同作用与先前发表的一项研究中肌电图得出的协同作用有许多相似之处，因此这意味着对两种不同类型实验数据的分析揭示了相似的潜在协同结构。
