Mechanical Engineering, University of Washington Seattle, WA, USA ; Sensorimotor Performance Program, Rehabilitation Institute of Chicago Chicago, IL, USA.
Front Comput Neurosci. 2013 Aug 8;7:105. doi: 10.3389/fncom.2013.00105. eCollection 2013.
One theory for how humans control movement is that muscles are activated in weighted groups or synergies. Studies have shown that electromyography (EMG) from a variety of tasks can be described by a low-dimensional space thought to reflect synergies. These studies use algorithms, such as nonnegative matrix factorization, to identify synergies from EMG. Due to experimental constraints, EMG can rarely be taken from all muscles involved in a task. However, it is unclear if the choice of muscles included in the analysis impacts estimated synergies. The aim of our study was to evaluate the impact of the number and choice of muscles on synergy analyses. We used a musculoskeletal model to calculate muscle activations required to perform an isometric upper-extremity task. Synergies calculated from the activations from the musculoskeletal model were similar to a prior experimental study. To evaluate the impact of the number of muscles included in the analysis, we randomly selected subsets of between 5 and 29 muscles and compared the similarity of the synergies calculated from each subset to a master set of synergies calculated from all muscles. We determined that the structure of synergies is dependent upon the number and choice of muscles included in the analysis. When five muscles were included in the analysis, the similarity of the synergies to the master set was only 0.57 ± 0.54; however, the similarity improved to over 0.8 with more than ten muscles. We identified two methods, selecting dominant muscles from the master set or selecting muscles with the largest maximum isometric force, which significantly improved similarity to the master set and can help guide future experimental design. Analyses that included a small subset of muscles also over-estimated the variance accounted for (VAF) by the synergies compared to an analysis with all muscles. Thus, researchers should use caution using VAF to evaluate synergies when EMG is measured from a small subset of muscles.
一种关于人类如何控制运动的理论认为,肌肉是按加权群组或协同作用激活的。研究表明,各种任务的肌电图 (EMG) 可以用一个低维空间来描述,这个空间被认为反映了协同作用。这些研究使用算法,如非负矩阵分解,从 EMG 中识别协同作用。由于实验限制,很少能从参与任务的所有肌肉中获取 EMG。然而,目前尚不清楚分析中包含的肌肉选择是否会影响估计的协同作用。我们研究的目的是评估肌肉数量和选择对协同分析的影响。我们使用肌肉骨骼模型来计算执行等长上肢任务所需的肌肉激活。从肌肉骨骼模型的激活中计算出的协同作用与先前的实验研究相似。为了评估分析中包含的肌肉数量的影响,我们随机选择了 5 到 29 个肌肉子集,并比较了从每个子集计算出的协同作用与从所有肌肉计算出的主协同作用的相似性。我们确定协同作用的结构取决于分析中包含的肌肉数量和选择。当分析中包含 5 个肌肉时,协同作用与主集的相似性仅为 0.57±0.54;然而,当包含 10 个以上肌肉时,相似性提高到 0.8 以上。我们确定了两种方法,从主集中选择优势肌肉或选择最大等长力最大的肌肉,这两种方法显著提高了与主集的相似性,并有助于指导未来的实验设计。当从一小部分肌肉测量 EMG 时,包含一小部分肌肉的分析也会高估协同作用所解释的方差 (VAF)。因此,当从一小部分肌肉测量 EMG 时,研究人员应谨慎使用 VAF 来评估协同作用。