Posts Tagged robot-assisted rehabilitation

[WEB SITE] New algorithm helps neurological disorder patients to walk naturally

Soon, wheelchairs may no longer be needed; new research enables patients with neurological disorders to walk again.

Millions of people cannot move their limbs as a result of a neurological disorder or having experienced an injury. But a newly developed algorithm, when coupled with robot-assisted rehabilitation, can help patients who had a stroke or a spinal cord injury to walk naturally.

In the United States, there are approximately 17,000 new cases of spinal cord injury (SCI) every year. Of these, 20 percent result in complete paraplegia (paralysis of the legs and lower half of body) and over 13 percent result in tetraplegia (paralysis of all four limbs).

But SCI is not the only reason that people experience this type of disability. Stroke, multiple sclerosis, cerebral palsy, and a range of other neurological disorders can all lead to paralysis. In fact, a recent survey estimated that in the U.S., almost 5.4 million people live with paralysis, with stroke being the leading cause of this disability.

Now, researchers from the National Centre of Competence in Research Robotics at École Polytechnique Fédérale de Lausanne (EPFL), and at the Lausanne University Hospital in Switzerland, have come up with a groundbreaking technology that may help these patients to regain their locomotor skills.

The scientists came up with an algorithm that helps a robotic harness to facilitate the movements of the patients, thus enabling them to move naturally.

The new research has been published in the journal Science Translational Medicine, and the first author of the study is Jean-Baptiste Mignardot.

Helping people to walk again

Current rehabilitation technologies for people with motor disabilities as a result of SCI or stroke involve walking on a treadmill, with the upper torso being supported by an apparatus. But existing technologies are either too rigid or do not allow the patients to move naturally in all directions.

As the authors of the new study explain, the challenge of locomotor rehabilitation resides in helping the nervous system to “relearn” the right movements. This is difficult due to the loss of muscle mass in the patients, as well as to the neurological wiring that has “forgotten” correct posture.

In order to overcome these obstacles and promote natural walking, Mignardot and colleagues designed an algorithm that coordinates with a robotic rehabilitation harness. The team tested the algorithm in more than 30 patients. The “smart walk assist” markedly and immediately improved the patients’ locomotor abilities.

This mobile harness, which is attached to the ceiling, enables patients to walk. This video shows how it works:

Additionally, after only 1 hour of training with the harness and algorithm, the “unsupported walking ability” of five of the patients improved considerably. By contrast, 1 hour on a conventional treadmill did not improve gait.

The researchers developed the so-called gravity-assist algorithm after carefully monitoring the movements of the patients and considering parameters such as “leg movement, length of stride, and muscle activity.”

As the authors explain, based on these measurements, the algorithm identifies the forces that must be applied to the upper half of the body in order to allow for natural walking.

The smart walk assist is an innovative body-weight support system because it manages to resist the force of gravity and push the patient back and forth, to the left and to the right, or in more of these directions at once, which recreates a natural gait and movement that the patients need in their day to day lives.

Grégoire Courtine, a neuroscientist at EPFL and the Lausanne University Hospital, comments on the significance of the findings, saying, “I expect that this platform will play a critical role in the rehabilitation of walking for people with neurological disorders.”

This is a smart, discreet, and efficient assistance that will aid rehabilitation of many persons with neurological disorders.”

Prof. Jocelyne Bloch, Department of Neurosurgery, Lausanne University Hospital

Source: New algorithm helps neurological disorder patients to walk naturally

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[Abstract] Quantitative EEG for Predicting Upper-limb Motor Recovery in Chronic Stroke Robot-assisted Rehabilitation – IEEE Xplore Document

Abstract:

Stroke is a leading cause for adult disability, which in many cases causes motor deficits. Despite the developments in motor rehabilitation techniques, recovery of upper limb functions after stroke is limited and heterogeneous in terms of outcomes, and knowledge of important factors that may affect the outcome of the therapy is necessary to make a reasonable prediction for individual patients.
In this study, we assessed the relationship between quantitative electroencephalographic (QEEG) measures and the motor outcome in chronic stroke patients that underwent a robot-assisted rehabilitation program to evaluate the utility of QEEG indices to predict motor recovery. For this purpose, we acquired resting-state electroencephalographic signals from which the Power Ratio Index (PRI), Delta/Alpha Ratio (DAR), and Brain Symmetry Index (BSI) were calculated. The outcome of the motor rehabilitation was evaluated using upper-limb section of the Fugl-Meyer Assessment.
We found that PRI was significantly correlated with the motor recovery, suggesting that this index may provide useful information to predict the rehabilitation outcome.

Source: Quantitative EEG for Predicting Upper-limb Motor Recovery in Chronic Stroke Robot-assisted Rehabilitation – IEEE Xplore Document

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[ARTICLE] An Original Classification of Rehabilitation Hand Exoskeletons – Full Text PDF

Abstract

The hand is an organ of grasping as well as sensation, communication, and fine dexterity. Since the 80’s, many researchers have been attempting to develop robotic devices aiming at replicating the functions of the human hand in the fields of industrial robotics, tele-manipulation, humanoid robotics, and upper limb prosthetics.

A special kind of robotic hand is the hand exoskeleton, that is directly attached to the human hand with the aim of providing assistance in motion/power generation. Hand exoskeletons are increasingly widespread in robot-based rehabilitation of patients suffering from different pathologies (in particular neurological diseases).

This paper reviews the state-of-the-art of hand exoskeletons developed for rehabilitation purposes and proposes a new systematic classification according to three key points related to the kinematic architecture: (i) mobility of a single finger exoskeleton, (ii) number of physical connections between the exoskeleton and the human finger phalanges, and (iii) way of integration of the exoskeleton mechanism with the human parts.

The discussion based upon the classification can be helpful to understand the reasons of adopting certain solutions for specific applications and the advantages and drawbacks of different designs, based on the work already done by other researchers.

The final purpose of the proposed classification is then to provide guidelines useful for the design of new hand exoskeletons on the basis of a systematic analysis. As an example, the solution designed, manufactured and clinically tested by the authors is reported.

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[ARTICLE] Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation – Full Text

Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects’ abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.

Introduction

Despite their participation in standard rehabilitation programs (Jørgensen et al., 1999; Dobkin, 2005), restoration of arm and hand function for activities of daily living is not achieved in the majority of stroke patients. In the first weeks and months after stroke, a positive relationship between the dose of therapy and clinically meaningful improvements has been demonstrated (Lohse et al., 2014; Pollock et al., 2014). In stroke patients with long-standing (>6 months) upper limb paresis, however, treatment effects were small, with no evidence of a dose-response effect of task-specific training on the functional capacity (Lang et al., 2016). This has implications for the use of assistive technologies such as robot-assisted training during stroke rehabilitation. These devices are usually applied to further increase and standardize the amount of therapy. They have the potential to improve arm/hand function and muscle strength, albeit currently available clinical trials provide on the whole only low-quality evidence (Mehrholz et al., 2015). It has, notably, been suggested that technology-assisted improvements during stroke rehabilitation might at least partially be due to unspecific influences such as increased enthusiasm for novel interventions on the part of both patients and therapists (Kwakkel and Meskers, 2014). In particular, a comparison between robot-assisted training and dose-matched conventional physiotherapy in controlled trials revealed no additional, clinically relevant benefits (Lo et al., 2010; Klamroth-Marganska et al., 2014). This might be related to saturation effects. Alternatively, the active robotic assistance might be too supportive when providing “assistance-as-needed” during the exercises (Chase, 2014). More targeted assistance might therefore be necessary during these rehabilitation exercises to maintain engagement without compromising the patients’ motivation; i.e., by providing only as much support as necessary and as little as possible (Grimm and Gharabaghi, 2016). In this context, passive gravity compensation with a multi-joint arm exoskeleton may be a viable alternative to active robotic assistance (Housman et al., 2009; Grimm et al., 2016a). In severely affected patients, performance-dependent, neuromuscular electrical stimulation of individual upper limb muscles integrated in the exoskeleton may increase the range of motion even further (Grimm and Gharabaghi, 2016; Grimm et al., 2016b). These approaches focus on the improvement of motor control, which is defined as the ability to make accurate and precise goal-directed movements without reducing movement speed (Reis et al., 2009; Shmuelof et al., 2012), or using compensatory movements (Kitago et al., 2013, 2015). Functional gains in hemiparetic patients, however, are often achieved by movements that aim to compensate the diminished range of motion of the affected limb (Cirstea and Levin, 2000; Grimm et al., 2016a). Although these compensatory strategies might be efficient in short-term task accomplishment, they may lead to long-term complications such as pain and joint-contracture (Cirstea and Levin, 2007; Grimm et al., 2016a). In this context, providing detailed information about how the movement is carried out, i.e., the quality of the movement, is more likely to recover natural movement patterns and avoid compensatory movements, than to provide information about movement outcome only (Cirstea et al., 2006; Cirstea and Levin, 2007; Grimm et al., 2016a). This feedback, however, needs to be provided implicitly, since explicit information has been shown to disrupt motor learning in stroke patients (Boyd and Winstein, 2004, 2006; Cirstea and Levin, 2007). Information on movement quality has therefore been incorporated as implicit closed-loop feedback in the virtual environment of an exoskeleton-based rehabilitation device (Grimm et al., 2016a). Specifically, the continuous visual feedback of the whole arm kinematics allowed the patients to adjust their movement quality online during each task; an approach closely resembling natural motor learning (Grimm et al., 2016a).

Along these lines, virtual reality and interactive video gaming have emerged as treatment approaches in stroke rehabilitation (Laver et al., 2015). They have been used as an adjunct to conventional care (to increase overall therapy time) or compared with the same dose of conventional therapy. These studies have demonstrated benefits in improving upper limb function and activities of daily living, albeit currently available clinical trials tend to provide only low-quality evidence (Laver et al., 2015). Most of these studies were conducted with mildly to moderately affected patients. In the remaining patient group with moderate to severe upper limp impairment, the intervention effects were more heterogeneous and affected by the impairment level, with either no or only modest additional gains in comparison to dose-matched conventional treatments (Housman et al., 2009; Byl et al., 2013; Subramanian et al., 2013).

With respect to the restoration of arm and hand function in severely affected stroke patients in particular, there is still a lack of evidence for additional benefits from technology-assisted interventions for activities of daily living. The only means of providing such evidence is by sufficiently powered, randomized and adequately controlled trials (RCT).

However, such high-quality RCT studies require considerable resources. Pilot data acquired earlier in the course of feasibility studies may provide the rationale and justification for later large-scale RCT. Such studies therefore need to demonstrate significant improvements, with functional relevance for the participating patients. Then again, costly RCT can be avoided when innovative interventions prove to be feasible but not effective with regard to the treatment goal, i.e., that they do not result in functionally relevant upper extremity improvements in severely affected stroke patients.

One recent pilot study, for example, applied brain signals to control an active robotic exoskeleton within the framework of a brain-robot interface (BRI) for stroke rehabilitation. This device provided patient control over the training device via motor imagery-related oscillations of the ipsilesional cortex (Brauchle et al., 2015). The study illustrated that a BRI may successfully link three-dimensional robotic training to the participant’s effort. Furthermore, the BRI allowed the severely impaired stroke patients to perform task-oriented activities with a physiologically controlled multi-joint exoskeleton. However, this approach did not result in significant upper limb improvements with functional relevance for the participating patients. This training approach was potentially too challenging and may even have frustrated the patients (Fels et al., 2015). The patients’ cognitive resources for coping with the mental load of performing such a neurofeedback task must therefore be taken into consideration (Bauer and Gharabaghi, 2015a; Naros and Gharabaghi, 2015). Mathematical modeling on the basis of Bayesian simulation indicates that this might be achieved when the task difficulty is adapted in the course of the training (Bauer and Gharabaghi, 2015b). Such an adaptation strategy has the potential to facilitate reinforcement learning (Naros et al., 2016b) by progressively challenging the patient (Naros and Gharabaghi, 2015). Recent studies explored automated adaptation of training difficulty in stroke rehabilitation of less severely affected patients (Metzger et al., 2014; Wittmann et al., 2015). More specifically, both robot-assisted rehabilitation of proprioceptive hand function (Metzger et al., 2014) and inertial sensor-based virtual reality feedback of the arm (Wittmann et al., 2015) benefit from assessment-driven adjustments of exercise difficulty. Furthermore, a direct comparison between adaptive BRI training and non-adaptive training (Naros et al., 2016b) or sham adaptation (Bauer et al., 2016a) in healthy patients revealed the impact of reinforcement-based adaptation for the improvement of performance. Moreover, the exercise difficulty has been shown to influence the learning incentive during the training; more specifically, the optimal difficulty level could be determined empirically while disentangling the relative contribution of neurofeedback specificity and sensitivity (Bauer et al., 2016b).

In the present 4-week pilot study, we combined these approaches and customized them for the requirements of patients with severe upper extremity impairment by applying a multi-joint exoskeleton for task-oriented arm and hand training in an adaptive virtual environment. Notably, due to the severity of their impairment, these patients were not able to practice the reach-to-grasp movements without the exoskeleton. The set-up was, however, limited to pure antigravity support, i.e., it provided passive rather than active assistance. Furthermore, it tested the feasibility of closed-loop online adaptation of exercise difficulty and aimed at automated progression of task challenge.

Continue —> Frontiers | Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation | Neuroprosthetics

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Figure 1. Training set-up with the exoskeleton (upper row) and the provided visual feedback in virtual reality (lower row).

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[ARTICLE] Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation – Full Text

Stroke patients with severe motor deficits of the upper extremity may practice rehabilitation exercises with the assistance of a multi-joint exoskeleton. Although this technology enables intensive task-oriented training, it may also lead to slacking when the assistance is too supportive. Preserving the engagement of the patients while providing “assistance-as-needed” during the exercises, therefore remains an ongoing challenge. We applied a commercially available seven degree-of-freedom arm exoskeleton to provide passive gravity compensation during task-oriented training in a virtual environment. During this 4-week pilot study, five severely affected chronic stroke patients performed reach-to-grasp exercises resembling activities of daily living. The subjects received virtual reality feedback from their three-dimensional movements. The level of difficulty for the exercise was adjusted by a performance-dependent real-time adaptation algorithm. The goal of this algorithm was the automated improvement of the range of motion. In the course of 20 training and feedback sessions, this unsupervised adaptive training concept led to a progressive increase of the virtual training space (p < 0.001) in accordance with the subjects’ abilities. This learning curve was paralleled by a concurrent improvement of real world kinematic parameters, i.e., range of motion (p = 0.008), accuracy of movement (p = 0.01), and movement velocity (p < 0.001). Notably, these kinematic gains were paralleled by motor improvements such as increased elbow movement (p = 0.001), grip force (p < 0.001), and upper extremity Fugl-Meyer-Assessment score from 14.3 ± 5 to 16.9 ± 6.1 (p = 0.026). Combining gravity-compensating assistance with adaptive closed-loop feedback in virtual reality provides customized rehabilitation environments for severely affected stroke patients. This approach may facilitate motor learning by progressively challenging the subject in accordance with the individual capacity for functional restoration. It might be necessary to apply concurrent restorative interventions to translate these improvements into relevant functional gains of severely motor impaired patients in activities of daily living.

Continue —> Frontiers | Closed-Loop Task Difficulty Adaptation during Virtual Reality Reach-to-Grasp Training Assisted with an Exoskeleton for Stroke Rehabilitation | Neuroprosthetics

www.frontiersin.org

Figure 1. Training set-up with the exoskeleton (upper row) and the provided visual feedback in virtual reality (lower row).

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[ARTICLE] Robot-Assisted Rehabilitation of Ankle Plantar Flexors Spasticity: A Three-Month Study with Proprioceptive Neuromuscular Facilitation – Full Text 

Abstract

In this paper, we aim to investigate the effect of Proprioceptive Neuromuscular Facilitation (PNF) based rehabilitation for ankle plantar flexors spasticity by using a Robotic Ankle-foot Rehabilitation System (RARS). A modified robot-assisted system was proposed and seven post-stroke patients with hemiplegic spastic ankles participated a three-month of robotic PNF training. Their impaired sides were used as the experimental group while their unimpaired sides as the control group. A robotic intervention for the experimental group generally started from a two minutes passive stretching to warm-up or relax the soleus and gastrocnemius muscle and also ended with the same one. Then a PNF training session included 30 trails was activated between them. The rehabilitation trainings were carried out three times a week as an addition of their regular rehabilitation exercise. Passive ankle joint range of motion, resistance torque and stiffness were measured in both ankles before and after the intervention. The changes in Achilles’ tendon length, walking speed, and lower limb function were also evaluated by the same physician or physiotherapist for each participant. Biomechanical measurements before interventions showed significant difference between the experimental group and the control group due to ankle spasticity. For the control group, there was no significant difference in the three months with no robotic intervention. But for the experimental group, passive dorsiflexion range of motion increased ($p0.05$). The robotic rehabilitation also improved the muscle strength ($p0.05$) and fast walking speed ($p<0.05$). These results indicated that PNF based robotic intervention could significantly alleviate lower limb spasticity and improve the motor function in chronic stroke participant. The robotic system could potentially be used as an effective tool in post-stroke rehabilitation training.

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Source: Frontiers | Robot-Assisted Rehabilitation of Ankle Plantar Flexors Spasticity: A Three-Month Study with Proprioceptive Neuromuscular Facilitation | Frontiers in Neurorobotics

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[ARTICLE] DEVELOPMENT OF A REHABILITATIVE EXOSKELETAL ARM – Full Text HTML/PDF

 

ABSTRACT
Reduced motor capacity of the upper extremities is a common
result of strokes, spinal cord injuries, accidental injuries, and
neurodegenerative diseases. Sensorimotor recovery can be
attained through gradual and repetitive exercises. In recent
years, robot-assisted rehabilitation has been shown to improve
treatment outcomes in these cases. This paper aims to discuss a
potential method of rehabilitation through the use of a robotic
exoskeletal device that is designed to conform to the shape of
an arm. Three different program methods were developed as
modes of exercise and therapy to achieve passive exercise,
assisted motions, and resistive-active exercise.
INTRODUCTION
Reduced motor capacity of the upper extremities is a
common result of strokes, spinal cord injuries, accidental
injuries, and neurodegenerative diseases. Sensorimotor
recovery can be attained through gradual and repetitive
exercises [3-5]. In recent years, robot-assisted rehabilitation has
been shown to improve treatment outcomes in these cases [6-
10].
Current exoskeleton rehabilitative devices have multiple
advantages over traditionally manual techniques, including [2]:
Data tracking for performance feedback
The ability to apply controlled forces at each joint as
well as magnitude adjustment of such forces based on
patient needs
They can be adjusted for multiple limb sizes to fit
different patients
They can replicate the majority of the patients upper
limb healthy workspace, using multiple degrees of
freedom.
This device contains additional advantages over current
devices. First of all it will be portable. It is going to address a
very specific task, which makes it more user friendly, and last
but not least it has a simple and cost effective design.
This bicep & tricep therapeutic device will have three
modes of operation: passive, assisted motions, and resistive-
active. A linear actuator provides the necessary movement of
the exoskeleton and a pair of force sensors tracks the response
of the patient to the therapeutic session. The passive mode is
for patients that have complete muscle atrophy. In this mode
the actuator does all the work to emotionally stimulate the
patient. The assisted motions mode offers the patient force
amplification. This mode allows patients with weak upper
limbs to perform everyday life tasks such as lifting, pushing,
pulling, etc. In this mode, the speed of the actuator is directly
proportional to the force applied by the user. If the patient
applies a higher force, the actuator moves faster, and vice versa.
In the resistive-active mode, the user must apply a load on the
load sensor that surpasses a certain threshold. When the robot
detects this, it moves the actuator at a speed that creates
resistance for the user. In this mode, if the load applied by the
user falls below the threshold, the actuator stops.

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[ARTICLE] Combining Robotic Training and Non-Invasive Brain Stimulation in Severe Upper Limb-Impaired Chronic Stroke Patients – Full Text HTML/PDF

Previous studies suggested that both robot-assisted rehabilitation and non-invasive brain stimulation can produce a slight improvement in severe chronic stroke patients. It is still unknown whether their combination can produce synergistic and more consistent improvements. Safety and efficacy of this combination has been assessed within a proof-of-principle, double-blinded, semi-randomized, sham-controlled trial. Inhibitory continuous Theta Burst Stimulation (cTBS) was delivered on the affected hemisphere, in order to improve the response to the following robot-assisted therapy via a homeostatic increase of learning capacity. Twenty severe upper limb-impaired chronic stroke patients were randomized to robot-assisted therapy associated with real or sham cTBS, delivered for 10 working days. Eight real and nine sham patients completed the study. Change in Fugl-Meyer was chosen as primary outcome, while changes in several quantitative indicators of motor performance extracted by the robot as secondary outcomes. The treatment was well-tolerated by the patients and there were no adverse events. All patients achieved a small, but significant, Fugl-Meyer improvement (about 5%). The difference between the real and the sham cTBS groups was not significant. Among several secondary end points, only the Success Rate (percentage of targets reached by the patient) improved more in the real than in the sham cTBS group. This study shows that a short intensive robot-assisted rehabilitation produces a slight improvement in severe upper-limb impaired, even years after the stroke. The association with homeostatic metaplasticity-promoting non-invasive brain stimulation does not augment the clinical gain in patients with severe stroke.

Introduction

Severe upper limb impairment in chronic stroke patients does not respond to standard rehabilitation strategies; for this reason there is the need of new treatments that might be effective in patients with drastically limited residual movement capacity. In patients with moderate to severe upper-limb impairment, a slight improvement have been reported using robot-assisted rehabilitative treatment, even years after a stroke (Lo et al., 2010). Another innovative approach for the enhancement of motor recovery is represented by non-invasive human brain stimulation techniques, such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS). These techniques can induce long-lasting changes in the excitability of central motor circuits via long-term potentiation/depression (LTP/LTD)-like phenomena (Di Pino et al., 2014b). A recent study reported a mild motor improvement after 10 sessions of rTMS in a group of severe chronic stroke patients (Demirtas-Tatlidedea et al., 2015).

Aim of present study was to explore whether the combination of these two approaches might enhance their positive effects on motor recovery. To the end of assessing safety and potential efficacy of the combination of robot-assisted rehabilitation and non-invasive brain stimulation in a group of chronic stroke patients with severe upper limb impairment, we designed a proof-of-principle double blinded semi-randomized sham-controlled trial. We used continuous theta burst stimulation (cTBS), a robust form of inhibitory rTMS inducing LTD-like changes lasting for about 1 h [8]. The choice of employing cTBS on the affected hemisphere was based on the findings of our recent study, which suggested that this inhibitory protocol can improve the response to physical therapy (Di Lazzaro et al., 2013). Moreover, rTMS protocols suppressing cortical excitability have been shown to strongly facilitate motor learning in normal subjects (Jung and Ziemann, 2009). Jung and Ziemann suggested that such enhancement might involve the phenomenon of “homeostatic” plasticity, which can be induced in the human brain using a variety of brain stimulation protocols (Karabanov et al., 2015). Considering the close link between LTP and mammalian learning and memory (Malenka and Bear, 2004), an enhancement of learning after LTD induction might appear a paradox. However, the experimental studies by Rioult-Pedotti et al. demonstrated the existence of a homeostatic balance between learning and the induction of LTP/LTD (Rioult-Pedotti et al., 2000), thus showing that the ease of producing synaptic LTP/LTD depends on the prior history of neural activity. In the context of stroke, this predicts that by delivering a rTMS protocol that induces LTD-like effects on the stroke-affected hemisphere before performing rehabilitation, would luckily result in better relearning (Di Pino et al., 2014a).

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Continue —> Frontiers | Combining Robotic Training and Non-Invasive Brain Stimulation in Severe Upper Limb-Impaired Chronic Stroke Patients | Neurodegeneration

Figure 1. Figurative illustration representing the algorithm of the study design, the evaluations carried out, and the treatments delivered. Treatment (real/sham cTBS + physical therapy) was delivered for 10 consecutive working days. Baseline evaluation was performed in the first day of treatment.

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[THESIS] Low-Cost, Intention-Detecting Robot to Assist the Movement of an Impaired Upper Limb – Full Text PDF

 

INTRODUCTION

1.1. Background

Despite advances in methods of prevention and rehabilitation associated with disabilitycausing conditions, a large portion of the world’s population continues to live with some degree of upper limb physical impairment. Stroke is the leading cause of such long-term disability in developed and developing countries, and the prevalence of stroke is increasing due to the aging of worldwide populations [1,2]. Approximately 795,000 persons in the United States suffer a new or recurrent stroke each year, and the combined direct and indirect costs of stroke exceed $34 billion annually [3]. With stroke having such a clear negative impact on individuals and society as a whole, many studies over the past few decades have been dedicated to determining better methods of alleviating the social, economic and physical burdens of stroke.

Recently, increased attention has been given to stroke rehabilitation, particularly robot assisted rehabilitation, in hopes of easing the difficulties associated with post-stroke life. Specific examples of robotic systems will be given in the following section, but in general robot assisted rehabilitation shows enormous promise in the field of rehabilitation due to its potential cost-effectiveness, adaptability, and mobility. Yet despite improving technology and continued development of effective robotic rehabilitative devices, vast numbers of stroke survivors continue to live with physical impairments. Among those who recover from stroke, only 10% recover completely, and many of the remaining survivors need rehabilitation due to continued impairments [4]. Approximately 50% of stroke survivors experience chronic hemiparesis (or weakness of one side of the body) and 26% become dependent in activities of daily living 2 (ADLs) [5]. These numbers depict a global population still in need of alternative forms of stroke relief.

Life-long physical impairment is not limited to stroke patients. Spinal cord injuries are another common cause of long-term disability. Approximately two million people worldwide live with a spinal-cord injury (SPI), nearly 250,000 of whom live in the United States [6]. Among them, 36.7% sustain paraplegia (impairment of lower limbs) and 52.2% sustain tetraplegia (impairment of all limbs and torso) [7]. Due, in part, to the physical barriers to basic mobility experienced by those with SPIs, the global unemployment rate of adults with spinal cord injury is over 60% [8]. Research studies show a rehabilitative environment that is continuously evolving and improving, but the numbers show a large population still experiencing a significantly reduced standard of living due to some form of physical impairment.

While pursuing better rehabilitation methods is a crucial endeavor, it is also important to acknowledge that many people need an alternative form of assistance for physical impairments, both while undergoing rehabilitation and in the unfortunate but common scenario of rehabilitation providing insufficient improvements. The aim of this thesis is to present a low cost robotic assistive device which may serve as a complement to rehabilitation procedures. The proposed system determines the intended movement of a user’s upper arm and assists said movement. In this manner, the system may provide immediate relief for someone suffering from physical impairments in their upper limbs, either as a complement to ongoing rehabilitation therapy or as a partial solution in the case of insufficient improvements from rehabilitation.

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[ARTICLE] A proposal for patient-tailored supervision of movement performance during end-effector-based robot-assisted rehabilitation of the upper extremities

Abstract

Millions of people worldwide suffer from stroke each year. One way to assist patients cost-effectively during their rehabilitation process is using end-effector-based robot-assisted rehabilitation. Such systems allow patients to use their own movement strategies to perform a movement task, which encourages them to do self-motivated training but also allow compensation movements if they have problems executing the movement tasks.

Therefore, a patient supervision system was developed on the basis of inertial measurement units and a patient-tailored movement interpretation system. Very light and small inertial measurement units were developed to record the patients’ movements during a teaching phase in which the desired movement is shown to the patient by a physiotherapist. During a following exercise phase, the patient is training the previously shown movement alone with the help of an end-effector-based robot-assisted rehabilitation system, and the patient’s movement is recorded again. The data from the teaching and exercise phases are compared with each other and evaluated by using fuzzy logic tailored to each patient. Experimental tests with one healthy subject and one stroke patient showed the capability of the system to supervise patient movements during the robot-assisted end-effector-based rehabilitation.

via A proposal for patient-tailored supervision of movement performance during end-effector-based robot-assisted rehabilitation of the upper extremities : Biomedical Engineering / Biomedizinische Technik.

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