Article

Rehabilitation of gait after stroke: a review towards a top-down approach. J Neuro Eng Rehabil 8(1):66

Instituto de Biomecánica de Valencia, Universitat Politécnica de Valencia, Valencia, Spain.
Journal of NeuroEngineering and Rehabilitation (Impact Factor: 2.62). 12/2011; 8(1):66. DOI: 10.1186/1743-0003-8-66
Source: PubMed

ABSTRACT This document provides a review of the techniques and therapies used in gait rehabilitation after stroke. It also examines the possible benefits of including assistive robotic devices and brain-computer interfaces in this field, according to a top-down approach, in which rehabilitation is driven by neural plasticity.
The methods reviewed comprise classical gait rehabilitation techniques (neurophysiological and motor learning approaches), functional electrical stimulation (FES), robotic devices, and brain-computer interfaces (BCI).
From the analysis of these approaches, we can draw the following conclusions. Regarding classical rehabilitation techniques, there is insufficient evidence to state that a particular approach is more effective in promoting gait recovery than other. Combination of different rehabilitation strategies seems to be more effective than over-ground gait training alone. Robotic devices need further research to show their suitability for walking training and their effects on over-ground gait. The use of FES combined with different walking retraining strategies has shown to result in improvements in hemiplegic gait. Reports on non-invasive BCIs for stroke recovery are limited to the rehabilitation of upper limbs; however, some works suggest that there might be a common mechanism which influences upper and lower limb recovery simultaneously, independently of the limb chosen for the rehabilitation therapy. Functional near infrared spectroscopy (fNIRS) enables researchers to detect signals from specific regions of the cortex during performance of motor activities for the development of future BCIs. Future research would make possible to analyze the impact of rehabilitation on brain plasticity, in order to adapt treatment resources to meet the needs of each patient and to optimize the recovery process.

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    • "String-Man, developed by Fraunhofer IPK Institut. In spite of the many state-of-the-art studies that already exist [6], the scientific value of robotic approach is still limited [5]. "
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    • "The current researches through sEMG signals predict the HMI for exoskeleton control that can be adjustable based on human-robot interaction improving the rehabilitation training [11].On the other hand, EEG signals have been explored very little in the control of lower limbs[12]. In neuro-control applications, bioelectrical potentials measured from the brain on primary motor areas and supplementary motor cortex have demonstrated high potentialities to stroke treatment a Brain Computer Interface (BCI) in neuro-rehabilitation applications [1]. The goal of this work is to analyze theHMIbased on EEG/sEMG from some daily activities related to knee motion in order to define in future worksa control strategy for a robotic system during gait rehabilitation using a residual motor skill of user.The following sections present the details of the robotic system proposed with their control strategy, then the experimental protocol used to acquire the EEG/sEMGsignals to identify patterns to control the knee exoskeleton based onHMI.Finally, the methods based on event-related desynchronization/synchronization (ERD/ERS) and slow cortical potential (SCP) from EEG signals,the feature extraction and classification pattern from sEMG signals to analyze the HMI are presented, including the discussion of the results. "
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