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Motor modules of human locomotion: influence of EMG averaging concatenation and number of step cycles

机译:人体运动的电机模块:EMG平均串联和步数的影响

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摘要

Locomotion can be investigated by factorization of electromyographic (EMG) signals, e.g., with non-negative matrix factorization (NMF). This approach is a convenient concise representation of muscle activities as distributed in motor modules, activated in specific gait phases. For applying NMF, the EMG signals are analyzed either as single trials, or as averaged EMG, or as concatenated EMG (data structure). The aim of this study is to investigate the influence of the data structure on the extracted motor modules. Twelve healthy men walked at their preferred speed on a treadmill while surface EMG signals were recorded for 60s from 10 lower limb muscles. Motor modules representing relative weightings of synergistic muscle activations were extracted by NMF from 40 step cycles separately (EMGSNG), from averaging 2, 3, 5, 10, 20, and 40 consecutive cycles (EMGAVR), and from the concatenation of the same sets of consecutive cycles (EMGCNC). Five motor modules were sufficient to reconstruct the original EMG datasets (reconstruction quality >90%), regardless of the type of data structure used. However, EMGCNC was associated with a slightly reduced reconstruction quality with respect to EMGAVR. Most motor modules were similar when extracted from different data structures (similarity >0.85). However, the quality of the reconstructed 40-step EMGCNC datasets when using the muscle weightings from EMGAVR was low (reconstruction quality ~40%). On the other hand, the use of weightings from EMGCNC for reconstructing this long period of locomotion provided higher quality, especially using 20 concatenated steps (reconstruction quality ~80%). Although EMGSNG and EMGAVR showed a higher reconstruction quality for short signal intervals, these data structures did not account for step-to-step variability. The results of this study provide practical guidelines on the methodological aspects of synergistic muscle activation extraction from EMG during locomotion.
机译:可以通过对肌电图(EMG)信号进行因子分解来研究运动,例如,使用非负矩阵因子分解(NMF)。这种方法是在特定步态阶段激活的运动模块中分布的肌肉活动的便捷简明表示。为了应用NMF,可将EMG信号分析为单次试验,平均EMG或级联EMG(数据结构)。这项研究的目的是研究数据结构对提取的电机模块的影响。 12名健康男人以他们喜欢的速度在跑步机上行走,同时从10条下肢肌肉记录了60秒钟的表面肌电信号。通过NMF分别从40个步周期(EMGSNG),平均2、3、5、10、20和40个连续周期(EMGAVR)以及同一组的串联中提取代表协同肌肉激活相对权重的运动模块连续周期(EMGCNC)。五个电机模块足以重建原始EMG数据集(重建质量> 90%),而与所使用的数据结构类型无关。但是,与EMGAVR相比,EMGCNC的重建质量略有降低。从不同的数据结构中提取出来的大多数电机模块都是相似的(相似度> 0.85)。但是,当使用来自EMGAVR的肌肉权重时,重建的40步EMGCNC数据集的质量很低(重建质量〜40%)。另一方面,使用EMGCNC的权重来重建此较长的运动提供了更高的质量,尤其是使用了20个级联步骤(重建质量约为80%)。尽管EMGSNG和EMGAVR在较短的信号间隔内显示出较高的重建质量,但这些数据结构并未说明逐步变化的原因。这项研究的结果为运动过程中从肌电信号中协同增效肌肉活化提取的方法学方面提供了实用指南。

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