Posts Tagged robot-assisted rehabilitation
[ARTICLE] Peak Activation Shifts in the Sensorimotor Cortex of Chronic Stroke Patients Following Robot-assisted Rehabilitation Therapy – Full Text
Ischemic stroke is the most common cause of complex chronic disability and the third leading cause of death worldwide. In recovering stroke patients, peak activation within the ipsilesional primary motor cortex (M1) during the performance of a simple motor task has been shown to exhibit an anterior shift in many studies and a posterior shift in other studies.
We investigated this discrepancy in chronic stroke patients who completed a robot-assisted rehabilitation therapy program.
Eight chronic stroke patients with an intact M1 and 13 Healthy Control (HC) volunteers underwent 300 functional magnetic resonance imaging (fMRI) scans while performing a grip task at different force levels with a robotic device. The patients were trained with the same robotic device over a 10-week intervention period and their progress was evaluated serially with the Fugl-Meyer and Modified Ashworth scales. Repeated measure analyses were used to assess group differences in locations of peak activity in the sensorimotor cortex (SM) and the relationship of such changes with scores on the Fugl-Meyer Upper Extremity (FM UE) scale.
Patients moving their stroke-affected hand had proportionally more peak activations in the primary motor area and fewer peak activations in the somatosensory cortex than the healthy controls (P=0.009). They also showed an anterior shift of peak activity on average of 5.3-mm (P<0.001). The shift correlated negatively with FM UE scores (P=0.002).
A stroke rehabilitation grip task with a robotic device was confirmed to be feasible during fMRI scanning and thus amenable to be used to assess plastic changes in neurological motor activity. Location of peak activity in the SM is a promising clinical neuroimaging index for the evaluation and monitoring of chronic stroke patients.
[ARTICLE] A randomized controlled trial on the effects induced by robot-assisted and usual-care rehabilitation on upper limb muscle synergies in post-stroke subjects – Full Text
Muscle synergies are hypothesized to reflect connections among motoneurons in the spinal cord activated by central commands and sensory feedback. Robotic rehabilitation of upper limb in post-stroke subjects has shown promising results in terms of improvement of arm function and motor control achieved by reassembling muscle synergies into a set more similar to that of healthy people. However, in stroke survivors the potentially neurophysiological changes induced by robot-mediated learning versus usual care have not yet been investigated. We quantified upper limb motor deficits and the changes induced by rehabilitation in 32 post-stroke subjects through the movement analysis of two virtual untrained tasks of object placing and pronation. The sample analyzed in this study is part of a larger bi-center study and included all subjects who underwent kinematic analysis and were randomized into robot and usual care groups. Post-stroke subjects who followed robotic rehabilitation showed larger improvements in axial-to-proximal muscle synergies with respect to those who underwent usual care. This was associated to a significant improvement of the proximal kinematics. Both treatments had negative effects in muscle synergies controlling the distal district. This study supports the definition of new rehabilitative treatments for improving the neurophysiological recovery after stroke.
Stroke is among the most frequent causes of adult-onset disability1, requiring a compelling medical and social need for rehabilitation. Among the most disabling post-stroke impairments are those affecting the contralesional upper limb, which include loss of movement, coordination, sensation, and dexterity. Even though substantial research efforts have been devoted to improve functional recovery2, motor rehabilitation in the upper extremity is still a challenging issue because of the limited understanding of the neurophysiological mechanisms underpinning motor recovery and the lack of interventions with demonstrated long term effectiveness2.
Since one of the primary goal of rehabilitation is to make patients independent, very often the training performed immediately after the stroke is focused on the recovery of walking. However, arm skills are also fundamental not only for activities that require fine movements such as grasping, manipulation, functional use of objects, but also for global abilities such as walking and balance reactions3,4. Furthermore, the non-recovery of the upper limb, which is often persistent, causes disabling conditions and is a major contributor to the reduced quality of life5,6.
The recovery after a stroke depends on a large repertoire of functional and structural processes within the central nervous system (CNS), named neuroplasticity, which may occur spontaneously but can also be induced by movement practice7.
Robot-assisted arm training has shown promising results for improving activities of daily living (ADLs), arm function, and arm muscle strength after stroke8,9. Randomized control trials (RCTs) have been carried out to clarify if robot-assisted therapy is able to produce better effects compared to usual care in terms of motor function improvement of the upper limb. The results suggest that these treatments show similar effectiveness in improving upper limb motor performance, as measured through clinical scales10,11. However, it should be noted that an instrumented analysis of upper limb movements during a functional task has recently shown that robot therapy induces larger improvements of shoulder/elbow coordination and greater reduction of compensatory movements than usual care treatments11. Overall, there are evidences that intensive, repetitive and functional motor exercises assist recovery and rehabilitation12. For this reason, robotic devices have been introduced in the rehabilitation field as tools to facilitate repetitive practice of limb movement, specifically in the upper extremity. The added value of robotic devices that support and guide the subject during the movement lays in the possibility of restoring neurophysiological pathways that are as much as possible similar to those of healthy subjects13,14,15. However, there is poor understanding of robot-induced motor learning in the CNS16. In addition, a still open question is whether the application of motor learning principles can enhance the transfer of planar robot-assisted rehabilitation effects also to non-trained 3D motor tasks, typical of ADLs17,18, that involve both proximal and distal parts of the upper limb.
The evaluation of behavioral parameters together with the measure of neurophysiological signals, such as the electromyographic activity (EMG), opens the possibility for a comprehensive characterization of motor control and consequently of its recovery after a neurological injury, providing useful insights for the definition of optimized and tailored rehabilitation programs.
Many studies support the hypothesis that the CNS solves the problem of coordinating the activation of several muscles to produce the multi-joint movements assembling a functional task, through the implementation of the so-called Muscle Synergies19,20,21. The latter are extracted from EMG signals and can represent the mechanism used by the cortical sensorimotor areas, brainstem and spinal cord to control groups of muscles concurrently activated to perform a motor task. Each synergy is constituted by two components: the muscle weightings and its temporal activation profiles, the location of which is assumed to be at different levels of the CNS, respectively, in the spinal cord and in cortical/subcortical sensorimotor structures. These units are functional structures related to specific motor patterns, defined as coordinated patterns of muscle activity that are combined flexibly to produce functional motor behaviors20,22. Muscle synergies approach has been already used on post-stroke patients both as a metric for motor assessment and to evaluate the effects of rehabilitation15,23, revealing that in sub-acute stroke survivors the altered muscle synergies of the paretic arm can be reassembled into those of healthy people following planar robot therapy15.
However, the potential changes in muscle synergies induced by robot-assisted therapy in stroke survivors and their difference with respect to those induced by usual care treatment have been poorly investigated and still remain unclear.
Considering all the above mentioned issues, this study reports the results of a prospective, randomized and single-blinded trial. The aim was to evaluate the changes in the motor control mechanisms of post-stroke subjects induced by robot-assisted planar training, with respect to those derived from usual care, during the execution of two non-trained motor tasks typically involved during activity of daily living (i.e. object placing onto a shelf and forearm pronation). We hypothesized that robot-assisted training might provide patients with a better restoring of neurophysiological patterns in terms of upper-limb muscle synergies than conventional therapy due to the strengthening of the specific brain plasticity and connectivity functions related to motor planning and execution13.[…]
[Abstract] A Single Session of Robot-Controlled Proprioceptive Training Modulates Functional Connectivity of Sensory Motor Networks and Improves Reaching Accuracy in Chronic Stroke
First Published December 29, 2018 Research Article
Background. Passive robot-generated arm movements in conjunction with proprioceptive decision making and feedback modulate functional connectivity (FC) in sensory motor networks and improve sensorimotor adaptation in normal individuals. This proof-of-principle study investigates whether these effects can be observed in stroke patients.
Methods. A total of 10 chronic stroke patients with a range of stable motor and sensory deficits (Fugl-Meyer Arm score [FMA] 0-65, Nottingham Sensory Assessment [NSA] 10-40) underwent resting-state functional magnetic resonance imaging before and after a single session of robot-controlled proprioceptive training with feedback. Changes in FC were identified in each patient using independent component analysis as well as a seed region–based approach. FC changes were related to impairment and changes in task performance were assessed.
Results. A single training session improved average arm reaching accuracy in 6 and proprioception in 8 patients. Two networks showing training-associated FC change were identified. Network C1 was present in all patients and network C2 only in patients with FM scores >7. Relatively larger C1 volume in the ipsilesional hemisphere was associated with less impairment (r = 0.83 for NSA, r = 0.73 for FMA). This association was driven by specific regions in the contralesional hemisphere and their functional connections (supramarginal gyrus with FM scores r = 0.82, S1 with NSA scores r = 0.70, and cerebellum with NSA score r = −0.82).
Conclusion. A single session of robot-controlled proprioceptive training with feedback improved movement accuracy and induced FC changes in sensory motor networks of chronic stroke patients. FC changes are related to functional impairment and comprise bilateral sensory and motor network nodes.
via A Single Session of Robot-Controlled Proprioceptive Training Modulates Functional Connectivity of Sensory Motor Networks and Improves Reaching Accuracy in Chronic Stroke – Shahabeddin Vahdat, Mohammed Darainy, Alexander Thiel, David J. Ostry, 2018
Purpose: This article aims to clarify the current state-of-the-art of robotic/mechanical devices for post-stroke thumb rehabilitation as well as the anatomical characteristics and motions of the thumb that are crucial for the development of any device that aims to support its motion.
Methods: A systematic literature search was conducted to identify robotic/mechanical devices for post-stroke thumb rehabilitation. Specific electronic databases and well-defined search terms and inclusion/exclusion criteria were used for such purpose. A reasoning model was devised to support the structured abstraction of relevant data from the literature of interest.
Results: Following the main search and after removing duplicated and other non-relevant studies, 68 articles (corresponding to 32 devices) were left for further examination. These articles were analyzed to extract data relative to (i) the motions assisted/permitted – either actively or passively – by the device per anatomical joint of the thumb and (ii) mechanical-related aspects (i.e., architecture, connections to thumb, other fingers supported, adjustability to different hand sizes, actuators – type, quantity, location, power transmission and motion trajectory).
Conclusions: Most articles describe preliminary design and testing of prototypes, rather than the thorough evaluation of commercially ready devices. Defining appropriate kinematic models of the thumb upon which to design such devices still remains a challenging and unresolved task. Further research is needed before these devices can actually be implemented in clinical environments to serve their intended purpose of complementing the labour of therapists by facilitating intensive treatment with precise and repeatable exercises.
- Implications for Rehabilitation
Post-stroke functional disability of the hand, and particularly of the thumb, significantly affects the capability to perform activities of daily living, threatening the independence and quality of life of the stroke survivors. The latest studies show that a high-dose intensive therapy (in terms of frequency, duration and intensity/effort) is the key to effectively modify neural organization and recover the motor skills that were lost after a stroke. Conventional therapy based on manual interaction with physical therapists makes the procedure labour intensive and increases the costs.
Robotic/mechanical devices hold promise for complementing conventional post-stroke therapy. Specifically, these devices can provide reliable and accurate therapy for long periods of time without the associated fatigue. Also, they can be used as a means to assess patients? performance and progress in an objective and consistent manner.
The full potential of robot-assisted therapy is still to be unveiled. Further exploration will surely lead to devices that can be well accepted equally by therapists and patients and that can be useful both in clinical and home-based rehabilitation practice such that motor recovery of the hand becomes a common outcome in stroke survivors.
This overview provides the reader, possibly a designer of such a device, with a complete overview of the state-of-the-art of robotic/mechanical devices consisting of or including features for the rehabilitation of the thumb. Also, we clarify the anatomical characteristics and motions of the thumb that are crucial for the development of any device that aims to support its motion.
Hopefully, this?combined with the outlined opportunities for further research?leads to the improvement of current devices and the development of new technology and knowledge in the field.
[Abstract] Development of an RBFN-based neural-fuzzy adaptive control strategy for an upper limb rehabilitation exoskeleton
The patients of paralysis with motion impairment problems require extensive rehabilitation programs to regain motor functions. The great labor intensity and limited therapeutic effect of traditional human-based manual treatment have recently boosted the development of robot-assisted rehabilitation therapy. In the present work, a neural-fuzzy adaptive controller (NFAC) based on radial basis function network (RBFN) is developed for a rehabilitation exoskeleton to provide human arm movement assistance. A comprehensive overview is presented to describe the mechanical structure and electrical real-time control system of the therapeutic robot, which provides seven actuated degrees of freedom (DOFs) and achieves natural ranges of upper extremity movement. For the purpose of supporting the disable patients to perform repetitive passive rehabilitation training, the RBFN-based NFAC algorithm is proposed to guarantee trajectory tracking accuracy with parametric uncertainties and environmental disturbances. The stability of the proposed control scheme is demonstrated through Lyapunov stability theory. Further experimental investigation, involving the position tracking experiment and the frequency response experiment, are conducted to compare the control performance of the proposed method to those of cascaded proportional-integral-derivative controller (CPID) and fuzzy sliding mode controller (FSMC). The comparison results indicate that the proposed RBFN-based NFAC algorithm is capable of obtaining lower position tracking error and better frequency response characteristic.
[Abstract] Evolution of upper limb kinematics four years after subacute robot-assisted rehabilitation in stroke patients
Purpose: To assess functional status and robot-based kinematic measures four years after subacute robot-assisted rehabilitation in hemiparesis.
Material and methods: Twenty-two patients with stroke-induced hemiparesis participated in a ≥3-month upper limb combined program of robot-assisted and occupational therapy from two months post-stroke, and received community-based therapy after discharge. Four years later, nineteen (86%) participated in this long-term follow-up study. Assessments two, five and 54 months post-stroke included Fugl-Meyer (FM), Modified Frenchay Scale (MFS, at Month 54) and robot-based kinematic measures of targeting tasks in three directions, north, paretic and non-paretic: distance covered, velocity, accuracy (RMS error from straight line) and smoothness (number of velocity peaks; upward changes in accuracy and smoothness measures represent worsening). Analysis was stratified by FM score at two months: ≥17 (Group 1) or < 17 (Group 2). Correlation between impairment (FM) and function (MFS) was explored at 54 months.
Results: Fugl-Meyer scores were stable from five to 54 months (+1[-2;4], median[1st;3rd quartiles], ns). Kinematic changes in the three directions pooled were: distance covered, -1[-17;2]% (ns); velocity, -8[-32;28]% (ns); accuracy, +6[-13;98]% (ns); smoothness, +44[-6;126]% (p<0.05). Group 2 showed decline vs Group 1 (p<0.001) in FM (Group 1, +3[1;5], p<0.01; Group 2, -7[-11;-1], ns) and accuracy (Group 1, -3[-27;38]%, ns; Group 2, +29[17;140]%, p<0.001). At 54 months, FM and MFS were highly correlated (Pearson’s rho = 0.89; p<0.001).
Conclusions: While impairment appeared stable four years after robot-assisted upper limb training during subacute post-stroke phase, kinematic performance deteriorated in spite of community-based therapy, especially in patients with more severe impairment.
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
[Abstract] Quantitative EEG for Predicting Upper-limb Motor Recovery in Chronic Stroke Robot-assisted Rehabilitation – IEEE Xplore Document
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.
[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.
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.