Department of Mechanical Engineering, Temple University, 1947 N. 12th Street, Philadelphia, PA, USA.
Department of Mechanical Engineering, University of Washington, 3900 E Stevens Way NE, Seattle, WA, USA.
J Neuroeng Rehabil. 2018 Jan 3;15(1):2. doi: 10.1186/s12984-017-0343-x.
Modules of muscle recruitment can be extracted from electromyography (EMG) during motions, such as walking, running, and swimming, to identify key features of muscle coordination. These features may provide insight into gait adaptation as a result of powered assistance. The aim of this study was to investigate the changes (module size, module timing and weighting patterns) of surface EMG data during assisted and unassisted walking in an powered, myoelectric, ankle-foot orthosis (ankle exoskeleton).
Eight healthy subjects wore bilateral ankle exoskeletons and walked at 1.2 m/s on a treadmill. In three training sessions, subjects walked for 40 min in two conditions: unpowered (10 min) and powered (30 min). During each session, we extracted modules of muscle recruitment via nonnegative matrix factorization (NNMF) from the surface EMG signals of ten muscles in the lower limb. We evaluated reconstruction quality for each muscle individually using R and normalized root mean squared error (NRMSE). We hypothesized that the number of modules needed to reconstruct muscle data would be the same between conditions and that there would be greater similarity in module timings than weightings.
Across subjects, we found that six modules were sufficient to reconstruct the muscle data for both conditions, suggesting that the number of modules was preserved. The similarity of module timings and weightings between conditions was greater then random chance, indicating that muscle coordination was also preserved. Motor adaptation during walking in the exoskeleton was dominated by changes in the module timings rather than module weightings. The segment number and the session number were significant fixed effects in a linear mixed-effect model for the increase in R with time.
Our results show that subjects walking in a exoskeleton preserved the number of modules and the coordination of muscles within the modules across conditions. Training (motor adaptation within the session and motor skill consolidation across sessions) led to improved consistency of the muscle patterns. Subjects adapted primarily by changing the timing of their muscle patterns rather than the weightings of muscles in the modules. The results of this study give new insight into strategies for muscle recruitment during adaptation to a powered ankle exoskeleton.
在运动过程中,如行走、跑步和游泳,可以从肌电图 (EMG) 中提取肌肉募集模块,以识别肌肉协调的关键特征。这些特征可能为动力辅助下的步态适应提供深入了解。本研究旨在探讨在动力、肌电、踝足矫形器(踝外骨骼)中,辅助和非辅助行走时表面肌电图数据的变化(模块大小、模块时间和加权模式)。
8 名健康受试者佩戴双侧踝外骨骼,在跑步机上以 1.2 m/s 的速度行走。在三个训练阶段,受试者在两种条件下行走 40 分钟:无动力(10 分钟)和动力(30 分钟)。在每个阶段,我们通过非负矩阵分解 (NNMF) 从下肢 10 块肌肉的表面肌电图信号中提取肌肉募集模块。我们使用 R 和归一化均方根误差 (NRMSE) 分别评估每个肌肉的重建质量。我们假设两种条件下重建肌肉数据所需的模块数量相同,并且模块时间的相似性大于加权的相似性。
在所有受试者中,我们发现 6 个模块足以重建两种条件下的肌肉数据,表明模块数量保持不变。模块时间和加权在条件之间的相似性大于随机机会,表明肌肉协调也得到了保留。在矫形器中行走时的运动适应主要由模块时间的变化而不是模块加权的变化决定。在一个线性混合效应模型中,节段数和阶段数是 R 随时间增加的显著固定效应。
我们的研究结果表明,在矫形器中行走的受试者在不同条件下保持了模块数量和模块内肌肉的协调。训练(同一阶段内的运动适应和不同阶段间的运动技能巩固)导致肌肉模式的一致性提高。受试者主要通过改变肌肉模式的时间而不是模块中肌肉的权重来适应。本研究的结果为适应动力踝外骨骼的肌肉募集策略提供了新的见解。