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.

Introduction: Over recent decades many researchers and clinicians have started to use Virtual Reality (VR) as a new technology for implementing innovative rehabilitation treatments in cognitive and motor domains. However, the expression ‘VR’ has often also been improperly used to refer to video games. Further, VR efficacy, often confused with that of video-game exercises, is still debated. Areas covered: In this review, we provide the scientific rationale for the advantages of using VR systems in rehabilitation and investigate whether the VR could really be a promising technique for the future of rehabilitation of patients, or if it is just an entertainment for scientists. In addition, we describe some of the most used devices in VR with their potential advantages for research and provide an overview of the recent evidence and meta-analyses in rehabilitation. Expert commentary: We highlight the efficacy and fallacies of VR in neurorehabilitation and discuss the important factors emerging from the use of VR, including the sense of presence and the embodiment over a virtual avatar, in developing future applications in cognitive and motor rehabilitation.

Introduction: The present review aims to provide an overview of the most common uses of wearable inertial sensors in the field of clinical human movement analysis. Areas covered: Six main areas of application are analysed: gait analysis, stabilometry, instrumented clinical tests, upper body mobility assessment, daily-life activity monitoring and tremor assessment. Each area is analyzed both from a methodological and applicative point of view. The focus on the methodological approaches is meant to provide an idea of the computational complexity behind a variable/parameter/index of interest so that the reader is aware of the reliability of the approach. The focus on the application is meant to provide a practical guide for advising clinicians on how inertial sensors can help them in their clinical practice. Expert commentary: Less expensive and more easy to use than other systems used in human movement analysis, wearable sensors have evolved to the point that they can be considered ready for being part of routine clinical routine.

Prevailing views on how we time the interception of a moving object assume that the visual inputs are informationally sufficient to estimate the time-to-contact from the object's kinematics. Here we present evidence in favor of a different view: the brain makes the best estimate about target motion based on measured kinematics and an a priori guess about the causes of motion. According to this theory, a predictive model is used to extrapolate time-to-contact from expected dynamics (kinetics). We projected a virtual target moving vertically downward on a wide screen with different randomized laws of motion. In the first series of experiments, subjects were asked to intercept this target by punching a real ball that fell hidden behind the screen and arrived in synchrony with the visual target. Subjects systematically timed their motor responses consistent with the assumption of gravity effects on an object's mass, even when the visual target did not accelerate. With training, the gravity model was not switched off but adapted to nonaccelerating targets by shifting the time of motor activation. In the second series of experiments, there was no real ball falling behind the screen. Instead the subjects were required to intercept the visual target by clicking a mousebutton. In this case, subjects timed their responses consistent with the assumption of uniform motion in the absence of forces, even when the target actually accelerated. Overall, the results are in accord with the theory that motor responses evoked by visual kinematics are modulated by a prior of the target dynamics. The prior appears surprisingly resistant to modifications based on performance errors.

Background. Robotic-assisted walking training after stroke aims to enable highly impaired patients to walk independently, but results have been mixed. Objective. The authors aimed to identify the characteristics of patients who may be most likely to benefit. Methods. A total of 48 participants with motor and gait dysfunction following subacute stroke were stratified by the motricity index into high (<29) and low (≥29) motor impairment groups. Each arm was randomized to a robotic or control group (RG or CG) at a mean of 20 days after stroke. All patients underwent 2 therapy sessions per day, 5 days per week for 3 months. Those in the RG underwent 20 sessions of robotic-assisted gait training in the first 4 weeks of inpatient therapy using controlled endpoint trajectories and abbreviated conventional therapy, whereas the CG received only conventional gait training. The primary outcome was the functional ambulation category (FAC), and secondary measures were the Rivermead mobility index (RMI) and 6-minute walking distance, all evaluated at hospital admission and at discharge. Results. The lower motricity group assigned to an electromechanical device significantly improved in the FAC (P < .001), RMI (P = .001), and walking distance (P = .029). Conventional and robotic therapies were equivalent in the higher motricity arm. Conclusion. Robotic therapy combined with conventional therapy may be more effective than conventional therapy alone in patients with greater motor impairment during inpatient stroke rehabilitation.

In this paper, we discuss robot-mediated neurorehabilitation as a significant emerging field in clinical medicine. Stroke rehabilitation is advancing toward more integrated processes, using robotics to facilitate this integration. Rehabilitation approaches have tremendous value in reducing long-term impairments in stroke patients during hospitalization and after discharge, of which robotic systems are a new modality that can provide more effective rehabilitation. The function of robotics in rehabilitative interventions has been examined extensively, generating positive yet not completely satisfactory clinical results. This article presents state-of-the-art robotic systems and their prospective function in poststroke rehabilitation of the upper and lower limbs.