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: 2.74). 12/2011; 8(1):66. DOI: 10.1186/1743-0003-8-66
Source: PubMed


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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    • "Robotic devices are increasingly investigated among researchers and are being used in the rehabilitation of physical impairments in both the upper and lower limbs [1] [2]. These devices provide safe, intensive and task-oriented rehabilitation for people with mild to severe motor impairments after neurologic injury. "

    ASME Dynamics Systems and Control Conference 2015; 10/2015
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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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    ABSTRACT: Several studies have examined whether robotic devices can exceed conventional gait training in terms of long term rehabilitation outcomes. Most of the research reported on promising results and pointed out the advantages of rehabilitation robots over strenuous manual physiotherapy. We present a novel cognitive system for gait training that assessed human behavior with body sensors, electromyography, joint kinematics and utilizes smart algorithms to control the level of joint support in the gait orthosis. The powered orthosis was tested in a single healthy person and several gait cycles were compared with free treadmill walking. The analysis was carried out with an optical measurement system and electromyography. Very similar gait patterns confirmed that the orthosis does not hinder the natural movement of the person. Additionally the functionality of the automated assistance level was tested with a healthy person emulating the bilateral crouch gait on the treadmill. The outcomes suggested that the powered gait orthosis does not hinder natural treadmill base movement and the control sufficiently enables the voluntary contribution of the person. However, additional tests will be carried out in neuromuscularly impaired persons before the system will be ready for a proof of concept study.
    ICORR International conference on rehabilitation robotics 2015, Singapore; 08/2015
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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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    ABSTRACT: The integration of lower limb exoskeletons with robotic walkers allows obtaining a system to improve mobility and security during gait rehabilitation. In this work, the evaluation of human motion intention (HMI) based on electroencephalogram (EEG) and surface electromyography (sEMG) signals are analyzed for a knee exoskeleton control as a preliminary study for gait neuro-rehabilitation with a hybrid robotic system. This system consists of the knee exoskeleton H2 and the UFES's Smart Walker, which are used to restore the neuromotor control function of subjects with neural injuries. An experimental protocol was developed to identify patterns to control the exoskeleton in accordance with the HMI-based on EEG/sEMG. The EEG and sEMGsignalsare recorded during thefollowing activities: stand-up/sit-down and knee flexion/extension. HMI is analyzed through both event-related desynchronization/synchronization (ERD/ERS) and slow cortical potential, as well as the myoelectric patternclassification related to lower limb. The feature extraction from sEMG signals is based on vector combinations in time and frequency domain which are used for a pattern classification stage trough an artificial neural network with LevenbergMarquadt training algorithm and support vector machine. Preliminary results shown that a combination of EEG/sEMG signals can be used to define a control strategy for the robotic system.
    6th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015, Las Vegas, EU; 07/2015
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