Aurich-Schuler Tabea, Labruyère Rob
Rehabilitation Center for Children and Adolescents, University Children's Hospital Zurich, Affoltern am Albis, Switzerland.
Children's Research Center, University Children's Hospital Zurich, Zurich, Switzerland.
Front Robot AI. 2019 Oct 31;6:109. doi: 10.3389/frobt.2019.00109. eCollection 2019.
Robot-assisted gait therapy is a fast-growing field in pediatric neuro-rehabilitation. Understanding how these constantly developing technologies work is a prerequisite for shaping clinical application. For the Lokomat, two new features are supposed to increase patients' movement variability and should enable a more physiological gait pattern: and . This work provides a secondary data analysis of a previously published study, and looks at surface electromyography (sEMG) during walking and six sub-conditions of and . The aim was to evaluate different levels of kinematic freedom on the gait pattern of pediatric patients by modulating settings of and . Fifteen patients (mean age 16 ± 2 years) with neurological gait disorders completed the measurements. We analyzed sEMG amplitudes and the correlation of sEMG patterns with normative data of five leg muscles during walking conditions with increasing kinematic freedom in the Lokomat. The new outcome measure of inter-step similarity is introduced as a proxy for walking task complexity. Within , sub-conditions showed significantly higher sEMG amplitudes in a majority of muscles with increasing kinematic freedom, and correlations with the norm pattern increased with increasing kinematic freedom. sub-conditions generally showed low or even negative correlations with the norm pattern and a lower inter-step similarity compared to . In general, this work highlights that the new hard- and software approaches of the Lokomat influence muscle activity differently. An increase of kinematic freedom of the walking condition led to an increase in muscular effort () or to a higher step variability () which can be interpreted as an increased task complexity of this condition. The inter-step similarity could be a helpful tool for the therapist to estimate the patient's state of strain.
机器人辅助步态治疗是小儿神经康复领域中一个快速发展的领域。了解这些不断发展的技术如何工作是塑造临床应用的先决条件。对于Lokomat而言，有两个新功能旨在增加患者的运动变异性，并应能实现更符合生理的步态模式： 和 。这项工作对先前发表的一项研究进行了二次数据分析，并观察了在 行走以及 和 的六个子条件下的表面肌电图（sEMG）。目的是通过调节 和 的设置来评估小儿患者步态模式上不同水平的运动自由度。15名患有神经性步态障碍的患者（平均年龄16±2岁）完成了测量。我们分析了在Lokomat中运动自由度增加的行走条件下，sEMG振幅以及sEMG模式与五条腿部肌肉的标准数据之间的相关性。引入了新的步间相似性结果测量指标，作为行走任务复杂性的替代指标。在 内，随着运动自由度增加，大多数肌肉的子条件显示出显著更高的sEMG振幅，并且与标准模式的相关性随着运动自由度增加而增加。 子条件通常与标准模式显示出低甚至负相关性，并且与 相比步间相似性更低。总体而言，这项工作强调Lokomat的新硬件和软件方法对肌肉活动的影响不同。行走条件下运动自由度的增加导致肌肉努力增加（ ）或步幅变异性增加（ ），这可以解释为该条件下任务复杂性增加。步间相似性可能是治疗师评估患者应变状态的有用工具。
