Cherni Yosra, Begon Mickael, Chababe Hicham, Moissenet Florent
Laboratoire de simulation et modélisation du mouvement, département de kinésiologie, université de Montréal, 1700, rue Jacques-Tétreault, H7N 0B6 Laval, Québec, Canada; Centre de recherche Marie-Enfant, CHU Sainte-Justine, 5200, rue Bélanger, H1T 1C9 Montréal, Québec, Canada.
Laboratoire de simulation et modélisation du mouvement, département de kinésiologie, université de Montréal, 1700, rue Jacques-Tétreault, H7N 0B6 Laval, Québec, Canada; Centre de recherche Marie-Enfant, CHU Sainte-Justine, 5200, rue Bélanger, H1T 1C9 Montréal, Québec, Canada.
Neurophysiol Clin. 2017 Sep;47(4):293-299. doi: 10.1016/j.neucli.2017.01.008. Epub 2017 Mar 16.
While generic protocols exist for gait rehabilitation using robotic orthotics such as the Lokomat, several settings - guidance, body-weight support (BWS) and velocity - may be adjusted to individualize patient training. However, no systematic approach has yet emerged. Our objective was to assess the feasibility and effects of a systematic approach based on electromyography to determine subject-specific settings with application to the strengthening of the gluteus maximus muscle in post-stroke hemiparetic patients.
Two male patients (61 and 65 years) with post-stroke hemiparesis performed up to 9 Lokomat trials by changing guidance and BWS while electromyography of the gluteus maximus was measured. For each subject, the settings that maximized gluteus maximus activity were used in 20 sessions of Lokomat training. Modified Functional Ambulation Classification (mFAC), 6-minutes walking test (6-MWT), and extensor strength were measured before and after training.
The greatest gluteus maximus activity was observed at (Guidance: 70% -BWS: 20%) for Patient 1 and (Guidance: 80% - BWS: 30%) for Patient 2. In both patients, mFAC score increased from 4 to 7. The additional distance in 6-MWT increased beyond minimal clinically important difference (MCID=34.4m) reported for post-stroke patients. The isometric strength of hip extensors increased by 43 and 114%.
Defining subject-specific settings for a Lokomat training was feasible and simple to implement. These two case reports suggest a benefit of this approach for muscle strengthening. It remains to demonstrate the superiority of such an approach for a wider population, compared to the use of a generic protocol.
虽然存在使用如Lokomat等机器人矫形器进行步态康复的通用方案，但一些参数设置——引导、体重支持（BWS）和速度——可进行调整以实现患者训练的个性化。然而，尚未出现系统的方法。我们的目的是评估基于肌电图的系统方法的可行性和效果，以确定针对特定个体的参数设置，并应用于中风后偏瘫患者臀大肌的强化训练。
两名中风后偏瘫男性患者（61岁和65岁）通过改变引导和BWS进行了多达9次Lokomat试验，同时测量臀大肌的肌电图。对于每个受试者，在20次Lokomat训练中使用使臀大肌活动最大化的参数设置。在训练前后测量改良功能性步行分类（mFAC）、6分钟步行试验（6-MWT）和伸肌力量。
患者1在引导70%-BWS 20%时观察到最大臀大肌活动，患者2在引导80%-BWS 30%时观察到最大臀大肌活动。在两名患者中，mFAC评分均从4提高到7。6-MWT中的额外行走距离增加超过了中风患者报告的最小临床重要差异（MCID = 34.4米）。髋伸肌的等长力量分别增加了43%和114%。
为Lokomat训练定义特定个体的参数设置是可行且易于实施的。这两个病例报告表明这种方法对肌肉强化有益。与使用通用方案相比，这种方法对更广泛人群的优越性仍有待证明。
