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外骨骼辅助地面行走的自动设置程序:中风人群的概念验证

Automatic Setting Procedure for Exoskeleton-Assisted Overground Gait: Proof of Concept on Stroke Population.

作者信息

Gandolla Marta, Guanziroli Eleonora, D'Angelo Andrea, Cannaviello Giovanni, Molteni Franco, Pedrocchi Alessandra

机构信息

Nearlab@Lecco, Polo territoriale di Lecco, Politecnico di Milano, Lecco, Italy.

Villa Beretta Rehabilitation Center, Valduce Hospital, Costa Masnaga, Italy.

出版信息

Front Neurorobot. 2018 Mar 19;12:10. doi: 10.3389/fnbot.2018.00010. eCollection 2018.

Abstract

Stroke-related locomotor impairments are often associated with abnormal timing and intensity of recruitment of the affected and non-affected lower limb muscles. Restoring the proper lower limbs muscles activation is a key factor to facilitate recovery of gait capacity and performance, and to reduce maladaptive plasticity. Ekso is a wearable powered exoskeleton robot able to support over-ground gait training. The user controls the exoskeleton by triggering each single step during the gait cycle. The fine-tuning of the exoskeleton control system is crucial-it is set according to the residual functional abilities of the patient, and it needs to ensure lower limbs powered gait to be the most physiological as possible. This work focuses on the definition of an automatic calibration procedure able to detect the best Ekso setting for each patient. EMG activity has been recorded from Tibialis Anterior, Soleus, Rectus Femoris, and Semitendinosus muscles in a group of 7 healthy controls and 13 neurological patients. EMG signals have been processed so to obtain muscles activation patterns. The mean muscular activation pattern derived from the controls cohort has been set as reference. The developed automatic calibration procedure requires the patient to perform overground walking trials supported by the exoskeleton while changing parameters setting. The Gait Metric index is calculated for each trial, where the closer the performance is to the normative muscular activation pattern, in terms of both relative amplitude and timing, the higher the Gait Metric index is. The trial with the best Gait Metric index corresponds to the best parameters set. It has to be noted that the automatic computational calibration procedure is based on the same number of overground walking trials, and the same experimental set-up as in the current manual calibration procedure. The proposed approach allows supporting the rehabilitation team in the setting procedure. It has been demonstrated to be robust, and to be in agreement with the current gold standard (i.e., manual calibration performed by an expert engineer). The use of a graphical user interface is a promising tool for the effective use of an automatic procedure in a clinical context.

摘要

与中风相关的运动障碍通常与受影响和未受影响的下肢肌肉募集的异常时间和强度有关。恢复下肢肌肉的正确激活是促进步态能力和表现恢复以及减少适应不良可塑性的关键因素。Ekso是一种可穿戴的动力外骨骼机器人,能够支持地面步态训练。用户通过在步态周期中触发每一步来控制外骨骼。外骨骼控制系统的微调至关重要——它是根据患者的残余功能能力设置的,并且需要确保下肢动力步态尽可能生理化。这项工作专注于定义一种自动校准程序,能够为每个患者检测出最佳的Ekso设置。在一组7名健康对照者和13名神经疾病患者中,记录了胫骨前肌、比目鱼肌、股直肌和半腱肌的肌电图活动。对肌电图信号进行了处理,以获得肌肉激活模式。将来自对照组的平均肌肉激活模式设定为参考。所开发的自动校准程序要求患者在改变参数设置的同时,在外骨骼支持下进行地面行走试验。为每个试验计算步态指标指数,在相对幅度和时间方面,表现越接近标准肌肉激活模式,步态指标指数就越高。步态指标指数最佳的试验对应最佳参数设置。需要注意的是,自动计算校准程序基于与当前手动校准程序相同数量的地面行走试验和相同的实验设置。所提出的方法能够在设置过程中为康复团队提供支持。已经证明它是稳健的,并且与当前的金标准(即由专家工程师进行的手动校准)一致。使用图形用户界面是在临床环境中有效使用自动程序的一个有前途的工具。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cc5f/5868134/60326587d5b8/fnbot-12-00010-g0001.jpg

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