Posts Tagged robotics
Next generation rehabilitation robotic and VR technology delivered by a team of first-class experts.
STEPS is leading the way in rehab technology and is home to an array of cutting edge robotic and virtual reality rehabilitation equipment. This world-leading technology assists with the intensive rehabilitation for people recovering from brain injury, spinal cord injury, strokes and complex trauma injuries.
The latest research in rehabilitation recognises that the best results are achieved through intensity of treatment. By combining the expertise of our clinicians and therapists with state-of-the-art rehabilitation technology, clients can maximise their progress and optimise their outcomes. We are the only place in the UK that gives clients access to rehabilitation robotic and VR technology in a residential setting.
• RehabHub™ – developed in Singapore by Fourier Intelligence, STEPS is the first place in the UK and only the second in Europe to offer clients access to this pioneering upper and lower limb robotic equipment, delivered in partnership with Thor Technologies.
• MindMaze – comprises two revolutionary virtual reality equipment, the MindMotion™ and MindMaze. Both were developed in Switzerland, and this trailblazing VR technology helps clients who have sustained a traumatic brain injury.
• Exoskeletons – STEPS is an assessment centre for the ReWalk™, ExoAtlet II and ReStore™ exoskeletons.
If we think you would benefit from using this specialist equipment as part of your residential rehab programme with us, an assessment will be carried out and a programme for the appropriate equipment will be created.
If you would like to find out more about booking an assessment or trial please contact us- 0114 258 7769.
[Abstract] Over-ground robotic lower limb exoskeleton in neurological gait rehabilitation: User experiences and effects on walking ability
BACKGROUND: Over-ground robotic lower limb exoskeletons are safe and feasible in rehabilitation with individuals with spinal cord injury (SCI) and stroke. Information about effects on stroke rehabilitees is scarce and descriptions of learning process and user experience is lacking.
OBJECTIVE: The objectives of this study were to describe how rehabilitees learn exoskeleton use, to study effects of exoskeleton assisted walking (EAW) training, and to study rehabilitees’ user experiences.
METHODS: One-group pre-test post-test pre-experimental study involved five rehabilitees with stroke or traumatic brain injury (TBI). Participants in chronic phase underwent twice a week an 8-week training intervention with Indego exoskeleton. Process of learning to walk and the level of assistance were documented. Outcome measurements were conducted with 6-minute and 10-meter walk tests (6 MWT, 10 mWT). User experience was assessed with a satisfaction questionnaire.
RESULTS: Rehabilitees learnt to walk using the exoskeleton with the assistance from 2–3 therapists within two sessions and progressed individually. Three participants improved their results in 10 mWT, four in 6 MWT. The rehabilitees felt comfortable and safe when using and exercising with the device.
CONCLUSION: Indego exoskeleton may be beneficial to gait rehabilitation with chronic stroke or TBI rehabilitees. The rehabilitees were satisfied with the exoskeleton as a rehabilitation device.
A brand new stroke rehabilitation pattern that could improve the treatment effect of stroke survivors.
A stroke occurs when the part of the brain loses its blood supply and stops working. Stroke is the leading cause of death and disability in the adult population. 75% of the surviving patients will lose the ability to move independently.
Traditional stroke rehabilitation equipment allows patients to follow the movements of the equipment latently. Henceforth, the rehabilitation effect is poor, and the patient’s willingness to train is likewise low.
Now, Tongji Hospital and BrainCo have introduced NCyborg Project, a new stroke rehabilitation pattern based on brain-computer interface (BCI) and brain-inspired intelligent robots. The project will mainly focus on:
- An algorithm for analyzing the movement intention of stroke patients based on brain-machine interface technology.
- A motion control strategy for a rehabilitation robot based on brain-inspired motion perception.
- The mechanism of stroke rehabilitation using brain-inspired intelligent robots.
The main aim of the NCyborg Project is to develop an easy-to-use, reliable and affordable stroke rehabilitation robot to improve the rehabilitation effect of stroke survivors, speed up the rehabilitation process, and reduce the costs.
The organization will start training the robot to support the rehabilitation of the hand. The robot is expected to recognize no less than eight hand movement intentions with a recognition accuracy of ≥ 90% and a response time ≤ of 300 ms.
Co-corresponding author, Jonh H. Zhang, explains: “The project’s goal is to develop an easy-to-use, reliable and affordable stroke rehabilitation robot that will improve the effect of rehabilitation for stroke survivors, speed up the rehabilitation process, and reduce the costs involved.”
Co-corresponding author, Bicheng Han, adds: “We hope that, within five years, millions of stroke patients will be using this product and see their lives improve.”
Co-corresponding author Zhouping Tang said, “the ‘N’ in the NCyborg Project name stands for ‘neural,’ while in fictional stories ‘cyborg’ is often an icon that is enhanced mentally and/or physically over and above the ‘norm’ with technology. In the real world, we believe that NCyborg Project will set up a brand-new stroke rehabilitation pattern which could qualitatively improve the treatment effect for stroke survivors.”
- Qi Huang et al. NCyborg Project – A new stroke rehabilitation pattern based on brain-computer interface. DOI: 10.1016/j.hest.2021.05.002
Approximately two thirds of stroke survivors maintain upper limb (UL) impairments and few among them attain complete UL recovery 6 months after stroke. Technological progress and gamification of interventions aim for better outcomes and constitute opportunities in self- and tele-rehabilitation.
Our objective was to assess the efficacy of serious games, implemented on diverse technological systems, targeting UL recovery after stroke. In addition, we investigated whether adherence to neurorehabilitation principles influenced efficacy of games specifically designed for rehabilitation, regardless of the device used.
This systematic review was conducted according to PRISMA guidelines (PROSPERO registration number: 156589). Two independent reviewers searched PubMed, EMBASE, SCOPUS and Cochrane Central Register of Controlled Trials for eligible randomized controlled trials (PEDro score ≥ 5). Meta-analysis, using a random effects model, was performed to compare effects of interventions using serious games, to conventional treatment, for UL rehabilitation in adult stroke patients. In addition, we conducted subgroup analysis, according to adherence of included studies to a consolidated set of 11 neurorehabilitation principles.
Meta-analysis of 42 trials, including 1760 participants, showed better improvements in favor of interventions using serious games when compared to conventional therapies, regarding UL function (SMD = 0.47; 95% CI = 0.24 to 0.70; P < 0.0001), activity (SMD = 0.25; 95% CI = 0.05 to 0.46; P = 0.02) and participation (SMD = 0.66; 95% CI = 0.29 to 1.03; P = 0.0005). Additionally, long term effect retention was observed for UL function (SMD = 0.42; 95% CI = 0.05 to 0.79; P = 0.03). Interventions using serious games that complied with at least 8 neurorehabilitation principles showed better overall effects. Although heterogeneity levels remained moderate, results were little affected by changes in methods or outliers indicating robustness.
This meta-analysis showed that rehabilitation through serious games, targeting UL recovery after stroke, leads to better improvements, compared to conventional treatment, in three ICF-WHO components. Irrespective of the technological device used, higher adherence to a consolidated set of neurorehabilitation principles enhances efficacy of serious games. Future development of stroke-specific rehabilitation interventions should further take into consideration the consolidated set of neurorehabilitation principles.
Each year more than 1 million Europeans suffer from stroke and approximately two-thirds of survivors maintain upper limb (UL) paresis . This number is expected to rise by 35% in upcoming years  leading to additional rehabilitation needs. To this date, few people attain complete UL recovery 6 months after stroke . New interventions targeting the UL aim for better outcomes in activities of daily living (ADL), functional independence and quality of life. Alongside conventional therapies, recent developments offer possibilities in self- and tele-rehabilitation  which could help manage, cost-efficiently , increasing rehabilitation demands.
Technological improvements in robot assisted therapy (RAT) and virtual reality (VR) systems (VRS) enhance patient care and facilitate therapist assistance during UL rehabilitation [6, 7]. First, RAT promotes the use of the affected limb, intensifies rehabilitation through task repetition and offers task-specific practice . Effectiveness of RAT is established for UL rehabilitation after stroke [8, 9]. Secondly, VRS provide augmented feedback, preserve motivation and are becoming cost-efficient . Recent meta-analyses suggest a superior effect of VR-based interventions compared to conventional treatment on UL function and activity after stroke, especially if developed for this specific purpose [10–12]. Authors attributed these findings to the fact that VRS specifically developed for rehabilitation, as opposed to commercial video-games (CVG), fulfil numerous neurorehabilitation principles.
Typically, a common denominator of VRS and RAT is playful interventions by means of serious games [13, 14]. A serious game is defined as a game that has education or rehabilitation as primary goal. These games combine entertainment, attentional engagement and problem solving to challenge function and performance [15, 16]. Moreover, they comply with several motor relearning principles that constitute the basis of effective interventions in neurorehabilitation [11, 16]. For example, some devices adapt game difficulty to stimulate recovery and maintain motivation . Others incorporate functional tasks mimicking ADL in virtual environments and provide performance feedback during and/or after task completion . Characteristics of serious games differ depending on targeted rehabilitation purposes and technical specificities of the system they are implemented on.
Previous work on the efficacy of VR-based interventions indicated that serious games may enhance UL recovery after stroke [11, 12, 18]. However, why such interventions, specifically developed for rehabilitation purposes and implemented on various types of devices (such as robots, smartphones, tablets, motion capture systems, etc.), may constitute effective therapies for UL rehabilitation after stroke needs to be further investigated. Recent theoretical research proposed consolidation of commonly acknowledged neurorehabilitation principles . Usually, serious games comply with several of these principles which creates an opportunity to evaluate clinical applicability of the consolidated set of principles. To this day, it remains unclear whether higher adherence to this consolidated set of neurorehabilitation principles enhances efficacy of interventions. In addition, it is not well known whether adherence to specific principles influences efficacy. Finally, rehabilitation effects on participation outcomes remain relatively unexplored. In this context, efficacy of interventions should be addressed in terms of all components of the World Health Organization’s International Classification of Function, Disability, and Health (ICF-WHO) model .
The main objective of this systematic review and meta-analysis was to address the following question in PICOS form: in adults after stroke (P), do serious games, implemented on various technological systems (I), show better efficacy than conventional treatment (C), to rehabilitate UL function and activity, and patient’s participation (O)? A secondary objective was to assess whether higher adherence to a consolidated set of neurorehabilitation principles enhances efficacy of games specifically designed for rehabilitation, irrespective of the technological device used.[…]
[ARTICLE] Feasibility and preliminary efficacy of a combined virtual reality, robotics and electrical stimulation intervention in upper extremity stroke rehabilitation – Full Text
Approximately 80% of individuals with chronic stroke present with long lasting upper extremity (UE) impairments. We designed the perSonalized UPper Extremity Rehabilitation (SUPER) intervention, which combines robotics, virtual reality activities, and neuromuscular electrical stimulation (NMES). The objectives of our study were to determine the feasibility and the preliminary efficacy of the SUPER intervention in individuals with moderate/severe stroke.
Stroke participants (n = 28) received a 4-week intervention (3 × per week), tailored to their functional level. The functional integrity of the corticospinal tract was assessed using the Predict Recovery Potential algorithm, involving measurements of motor evoked potentials and manual muscle testing. Those with low potential for hand recovery (shoulder group; n = 18) received a robotic-rehabilitation intervention focusing on elbow and shoulder movements only. Those with a good potential for hand recovery (hand group; n = 10) received EMG-triggered NMES, in addition to robot therapy. The primary outcomes were the Fugl-Meyer UE assessment and the ABILHAND assessment. Secondary outcomes included the Motor Activity Log and the Stroke Impact Scale.
Eighteen participants (64%), in either the hand or the shoulder group, showed changes in the Fugl-Meyer UE or in the ABILHAND assessment superior to the minimal clinically important difference.
This indicates that our personalized approach is feasible and may be beneficial in improving UE function in individuals with moderate to severe impairments due to stroke.
Approximately 80% of individuals with stroke experience hemiparesis of the upper extremity (UE)  leading to chronic impairments such as weakness, loss of motor control, edema, pain and spasticity. These have important consequences for quality of life as impairments in hand and arm function limit participation in activities of daily living [2, 3]. Accordingly, recovery of UE function is seen as highly important by individuals with chronic stroke, caregivers and rehabilitation professionals .
According to the Canadian Stroke Best Practices , UE rehabilitation should involve the affected limb in “training that is meaningful, engaging, repetitive, progressively adapted, task-specific and goal-oriented”. Advances in rehabilitation technology, in particular robotics, virtual reality (VR) and neuromuscular electrical stimulation (NMES), have been shown to be individually effective for improving UE function of individuals with stroke, through the provision of such repetitive and task-oriented training. Robotic devices can be used to assist individuals who are unable to complete arm movements by themselves . Robotic rehabilitation has demonstrated functional gains in individuals with mild and moderate stroke impairments [7,8,9]. Likewise, some of our recent work  has shown that individuals with severe, chronic stroke can improve their arm range of motion and clinical scores after ten sessions of robotic therapy. However, it should be noted that functional gains in robotic therapy are not greater than those obtained with similar intensity conventional therapy . While the intensity of practice is a determining factor in stroke recovery, higher improvements might have been achieved by robotic therapy if its focus was not only on shoulder and elbow movements, but also on hand function. This may be possible by integrating robotic therapy in a rehabilitation program that also includes other modalities that better target hand function.
VR activities constitute another approach to UE stroke rehabilitation, where patients typically perform movements without physical assistance. Reviews examining the use of VR for the improvement of UE function show promising results [11, 12]. In our view, VR could consolidate the UE functional gains obtained through robotic rehabilitation. While most VR activities typically focus on shoulder and elbow movements, some recent technical advances now allow the inclusion of hand movements as well. Specifically, the Microsoft Kinect version 2, used to track movements in VR, can detect hand opening and closing in addition to shoulder, elbow and wrist movements. These capabilities have been included in a new rehabilitation application, targeting UE reaching and grasping movements , which was part of our rehabilitation approach.
Electromyographically (EMG)-triggered NMES is a muscle stimulation modality that has been used to facilitate motor recovery of the hand after stroke . The individual with stroke needs to activate the muscle(s) volitionally to trigger the NMES . Thus, EMG-triggered NMES provides wrist and/or finger extension time-locked to the cognitive movement intent to actively extend the wrist and open the hand, making the training ecological and functionally relevant. EMG-triggered NMES has been shown to improve voluntary activation of isolated muscles, particularly in task-specific patterns .
While advances in robotics, VR and NMES have led to new treatment modalities targeting UE function post-stroke, further progress is needed for these technologies to have a true impact. Despite numerous studies attempting to identify the most effective rehabilitation interventions, post-stroke UE recovery remains disappointing  with sensorimotor deficits persisting in a large proportion of stroke survivors for more than 6 months (up to 62% ). Improvements in clinical scores have been small and often fail to meet the criteria for minimal clinically important differences (MCID) . While most of the recent clinical trials have failed to demonstrate improvements on UE function that favour new interventions such as robotics or VR, over conventional, dose-matched therapy , combination of different modalities may have a greater impact on stroke recovery than each individual modality alone . Thus, there is a need to look beyond the ‘one-size-fits-all’ approach, where a single UE modality is applied to a group of post-stroke individuals. Another possible reason for the relatively small gains in UE function, and in particular the low gains in hand function , is that an individual’s potential for recovery is not always considered . In clinical practice, therapists typically prescribe UE exercises to their clients based on initial clinical measures, which turn out to be poor predictors of future UE function . However, assessing the integrity of the affected corticospinal tract (CST), by means of motor evoked potentials (MEPs) elicited by non-invasive transcranial magnetic stimulation (TMS), was found to strongly predict the changes in UE function that could be elicited by rehabilitation . In particular, the work by Milot et al.  showed that amongst several brain measures (e.g., magnetic resonance imaging, diffusion tensor imaging), baseline MEP amplitude was the best predictor of the response to robotic training of the affected UE in chronic stroke survivors. The presence of a MEP indicates that the CST, linking the motor areas of the brain to the hand musculature, is at least partially preserved.
Considering that (1) an individualized intervention to post stroke UE rehabilitation is desirable, (2) CST integrity is a strong predictor of hand function recovery, and (3) combination of different modalities may have a greater impact on stroke recovery than each individual modality alone, our proposed approach was to combine multiple modalities in an individualized intervention, tailored to each stroke participant’s functional status and recovery potential. Recovery may be enhanced by first assessing CST integrity in order to determine the potential for recuperating hand function, and then combining multiple purposefully selected combinations of modalities to target motor deficits of each individual. Specifically, our perSonalized UPper Extremity Rehabilitation (SUPER) program included: (1) robotic activities to work on physically assisted UE reaching movements; (2) VR activities to work on unassisted reaching and grasping movements; and (3) NMES to facilitate hand opening and closing movements. The frequency of incorporation of each modality during the intervention was determined according to the individual’s potential for hand recovery. Our objectives were to determine the feasibility and the treatment effect of the SUPER program in individuals with moderate/severe chronic stroke. Our hypotheses were that (1) the SUPER program would be feasible in terms of process, resources, management and safety indicators and (2) stroke participants with a low potential for hand recovery would benefit from a shoulder/elbow-centered intervention, while those with a high potential would benefit from an intervention involving the whole arm.[…]
[Abstract] Robotic assistive and rehabilitation devices leading to motor recovery in upper limb: a systematic review
Stroke, spinal cord injury and other neuromuscular disorders lead to impairments in the human body. Upper limb impairments, especially hand impairments affect activities of daily living (ADL) and reduce the quality of life. The purpose of this review is to compare and evaluate the available robotic rehabilitation and assistive devices that can lead to motor recovery or maintain the current motor functional level.
A systematic review was conducted of the literature published in the years from 2016–2021, to focus on the most recent rehabilitation and assistive devices available in the market or research environments.
A total of 230 studies published between 2016 and 2021 were identified from various databases. 107 were excluded with various reasons. Twenty-eight studies were taken into detailed review, to determine the efficacy of robotic devices in improving upper limb impairments or maintaining the current level from getting worse.
It was concluded that with a good strategy and treatment plan; appropriate and regular use of these robotic rehabilitation and assistive devices do lead to improvements in current conditions of most of the subjects and prolonged use may lead to motor recovery.
- Implications for Rehabilitation
- Stroke, accidents, spinal cord injuries and other neuromuscular disorders lead to impairments. Upper limb impairments have a tremendous adverse affect on ADL and reduces quality of life drastically.
- Advancement in technology has led to the designing of many robotic assistive and rehabilitation devices to assist in motor recovery or aid in ADL.
- This review analyses different available devices for rehabilitation and assistance and points out that use of these devices in time does help in motor recovery. Most of the studies reviewed showed improvements for the user.
- Future devices should be more portable and easier to use from home,
[ARTICLE] Effect of assist-as-needed robotic gait training on the gait pattern post stroke: a randomized controlled trial – Full Text
Regaining gait capacity is an important rehabilitation goal post stroke. Compared to clinically available robotic gait trainers, robots with an assist-as-needed approach and multiple degrees of freedom (AANmDOF) are expected to support motor learning, and might improve the post-stroke gait pattern. However, their benefits compared to conventional gait training have not yet been shown in a randomized controlled trial (RCT). The aim of this two-center, assessor-blinded, RCT was to compare the effect of AANmDOF robotic to conventional training on the gait pattern and functional gait tasks during post-stroke inpatient rehabilitation.
Thirty-four participants with unilateral, supratentorial stroke were enrolled (< 10 weeks post onset, Functional Ambulation Categories 3–5) and randomly assigned to six weeks of AANmDOF robotic (combination of training in LOPES-II and conventional gait training) or conventional gait training (30 min, 3–5 times a week), focused on pre-defined training goals. Randomization and allocation to training group were carried out by an independent researcher. External mechanical work (WEXT), spatiotemporal gait parameters, gait kinematics related to pre-defined training goals, and functional gait tasks were assessed before training (T0), after training (T1), and at 4-months follow-up (T2).
Two participants, one in each group, were excluded from analysis because of discontinued participation after T0, leaving 32 participants (AANmDOF robotic n = 17; conventional n = 15) for intention-to-treat analysis. In both groups, WEXT had decreased at T1 and had become similar to baseline at T2, while gait speed had increased at both assessments. In both groups, most spatiotemporal gait parameters and functional gait tasks had improved at T1 and T2. Except for step width (T0–T1) and paretic step length (T0–T2), there were no significant group differences at T1 or T2 compared to T0. In participants with a pre-defined goal aimed at foot clearance, paretic knee flexion improved more in the AANmDOF robotic group compared to the conventional group (T0–T2).
Generally, AANmDOF robotic training was not superior to conventional training for improving gait pattern in subacute stroke survivors. Both groups improved their mechanical gait efficiency. Yet, AANmDOF robotic training might be more effective to improve specific post-stroke gait abnormalities such as reduced knee flexion during swing.
Trial registration Registry number Netherlands Trial Register (www.trialregister.nl): NTR5060. Registered 13 February 2015.
Regaining gait capacity is one of the most reported rehabilitation goals post stroke [1,2,3]. Besides basic gait independence and the ability to adapt gait to environmental demands, rehabilitation is often focused on optimizing the individual gait pattern, particularly in the early phase post stroke. After unilateral supratentorial stroke, the hemiparetic gait pattern is commonly characterized by pes equinovarus during swing and/or loading , knee instability during early and/or midstance [5, 6], impaired ankle plantarflexion power during push-off , and reduced knee flexion during (pre)swing of the paretic leg . As a consequence, asymmetry in step length  and/or single support time are observed in many patients with post-stroke hemiparesis . In addition, hemiparetic gait is associated with reduced gait speed , increased fall risk , and limited community ambulation . Hence, improving the post-stroke gait pattern is an important rehabilitation goal.
Robotic gait training has the potential to improve the post-stroke gait pattern [11,12,13,14,15,16,17], but its benefits compared to conventional gait training are still under debate [11,12,13,14,15,16,17,18]. Most clinically available robotic gait trainers lack the ability to adjust the robotic actuation based on the user’s performance, which may restrain motor learning . In contrast, robotic gait trainers with a so called ‘assist-as-needed’ (AAN) approach adapt guidance to the user’s needs [19, 20] and allow support of specific subtasks of the gait cycle , thereby promoting active involvement of the user and, thus, motor learning [21,22,23]. Furthermore, robotic gait trainers with ample degrees of freedom allow a (near) normal gait pattern, in particular with respect to active balance control during walking [21, 24]. In addition, sufficient allowance of movement variability optimizes the amount of error information needed for motor learning . Consequently, robotic gait training with AAN principles and multiple degrees of freedom (AANmDOF) has the potential to improve gait post stroke. However, no evidence from randomized controlled trials is yet available for its superiority compared to conventional gait training, in particular with regard to the gait pattern, during primary inpatient stroke rehabilitation.
As nearly all kinematic gait deviations and/or spatiotemporal gait abnormalities are translated into irregular movements of the body center of mass of the body (COM), we evaluated the quality of the post-stroke gait pattern based on the COM trajectory. COM movement relative to its surroundings is represented by external mechanical work (WEXT) . In healthy individuals who walk at their preferred speed, COM movements in directions other than the walking direction are typically minimized , and WEXT is relatively small. Stroke survivors, however, often show compensatory movements in the frontal, sagittal and/or transversal planes while walking, resulting in irregular and enlarged COM trajectories  and increased WEXT , reflecting a reduced quality of the gait pattern. As increased gait speed is generally associated with increased WEXT [30, 31], interpretation of changes in WEXT should be related to changes in gait speed.
The primary aim of the present study was to evaluate whether six weeks AANmDOF robotic gait training would be superior to conventional gait training in terms of WEXT in stroke survivors during their inpatient rehabilitation. A secondary aim was to evaluate whether this effect would be retained four months after the intervention. We hypothesized that, given a similar increase in gait speed between groups, the increase in WEXT would be smaller following robotic training compared to conventional training one week and four months after the intervention period. A third aim was to evaluate the AANmDOF robotic gait training on spatiotemporal gait parameters, kinematics related to pre-defined training goals, and functional gait tasks.[…]
[REVIEW] Brain–computer interface robotics for hand rehabilitation after stroke: a systematic review – Full Text
Hand rehabilitation is core to helping stroke survivors regain activities of daily living. Recent studies have suggested that the use of electroencephalography-based brain-computer interfaces (BCI) can promote this process. Here, we report the first systematic examination of the literature on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profile these systems from a technical and clinical perspective.
A search for January 2010–October 2019 articles using Ovid MEDLINE, Embase, PEDro, PsycINFO, IEEE Xplore and Cochrane Library databases was performed. The selection criteria included BCI-hand robotic systems for rehabilitation at different stages of development involving tests on healthy participants or people who have had a stroke. Data fields include those related to study design, participant characteristics, technical specifications of the system, and clinical outcome measures.
30 studies were identified as eligible for qualitative review and among these, 11 studies involved testing a BCI-hand robot on chronic and subacute stroke patients. Statistically significant improvements in motor assessment scores relative to controls were observed for three BCI-hand robot interventions. The degree of robot control for the majority of studies was limited to triggering the device to perform grasping or pinching movements using motor imagery. Most employed a combination of kinaesthetic and visual response via the robotic device and display screen, respectively, to match feedback to motor imagery.
19 out of 30 studies on BCI-robotic systems for hand rehabilitation report systems at prototype or pre-clinical stages of development. We identified large heterogeneity in reporting and emphasise the need to develop a standard protocol for assessing technical and clinical outcomes so that the necessary evidence base on efficiency and efficacy can be developed.
There is growing interest in the use of robotics within the field of rehabilitation. This interest is driven by the increasing number of people requiring rehabilitation following problems such as stroke (with an ageing population), and the global phenomenon of insufficient numbers of therapists able to deliver rehabilitation exercises to patients [1, 2]. Robotic systems allow a therapist to prescribe exercises that can then be guided by the robot rather than the therapist. An important principle within the use of such systems is that the robots assist the patient to actively undertake a prescribed movement rather than the patient’s limb being moved passively. This means that it is necessary for the system to sense when the patient is trying to generate the required movement (given that, by definition, the patient normally struggles with the action). One potential solution to this issue is to use force sensors that can detect when the patient is starting to generate the movement (at which point the robot’s motors can provide assistive forces). It is also possible to use measures of muscle activation (EMGs) to detect the intent to move . In the last two decades there has been a concerted effort by groups of clinicians, neuroscientists and engineers to integrate robotic systems with brain signals correlated with a patient trying to actively generate a movement, or imagine a motor action, to enhance the efficacy and effectiveness of stroke rehabilitation- these systems fall under the definition of Brain Computer Interfaces, or BCIs .
BCIs allow brain state-dependent control of robotic devices to aid stroke patients during upper limb therapy. While BCIs in their general form have been in development for almost 50 years  and were theoretically made possible since the discovery of the scalp-recorded human electroencephalogram (EEG) in the 1920s , their application to rehabilitation is more recent [7,8,9]. Graimann et al.  defined a BCI as an artificial system that provides direct communication between the brain and a device based on the user’s intent; bypassing the normal efferent pathways of the body’s peripheral nervous system. A BCI recognises user intent by measuring brain activity and translating it into executable commands usually performed by a computer, hence the term “brain–computer interface”.
Most robotic devices used in upper limb rehabilitation exist in the form of exoskeletons or end-effectors. Robotic exoskeletons (i.e., powered orthoses, braces) are wearable devices where the actuators are biomechanically aligned with the wearer’s joints and linkages; allowing the additional torque to provide assistance, augmentation and even resistance during training . In comparison, end-effector systems generate movement through applying forces to the most distal segment of the extremity via handles and attachments . Rehabilitation robots are classified as Class II-B medical devices (i.e., a therapeutic device that administers the exchange of energy, mechanically, to a patient) and safety considerations are important during development [12, 13]. Most commercial robots are focused on arms and legs, each offering a unique therapy methodology. There is also a category of device that target the hand and finger. While often less studied than the proximal areas of the upper limb, hand and finger rehabilitation are core component in regaining activities of daily living (ADL) . Many ADLs require dexterous and fine motor movements (e.g. grasping and pinching) and there is evidence that even patients with minimal proximal shoulder and elbow control can regain some hand capacity long-term following stroke .
The strategy of BCI-robot systems (i.e. systems that integrate BCI and robots into one unified system) in rehabilitation is to recognise the patient’s intention to move or perform a task via a neural or physiological signal, and then use a robotic device to provide assistive forces in a manner that mimics the actions of a therapist during standard therapy sessions . The resulting feedback is patient-driven and is designed to aid in closing the neural loop from intention to execution. This process is said to promote use-dependent neuroplasticity within intact brain regions and relies on the repeated experience of initiating and achieving a specified target [17, 18]; making the active participation of the patient in performing the therapy exercises an integral part of the motor re-learning process [19, 20].
The aforementioned scalp-recorded EEG signal is a commonly used instrument for data acquisition in BCI systems because it is non-invasive, easy to use and can detect relevant brain activity with high temporal resolution [21, 22]. In principle, the recognition of motor imagery (MI), the imagination of movement without execution, via EEG can allow the control of a device independent of muscle activity . It has been shown that MI-based BCI can discriminate motor intent by detecting event-related spectral perturbations (ERSP) [23, 24] and/or event-related desynchronisation/synchronisation (ERD/ERS) patterns in the µ (9–11 Hz) and β (14–30 Hz) sensorimotor rhythm of EEG signals . However, EEG also brings with it some challenges. These neural markers are often concealed by various artifacts and may be difficult to recognise through the raw EEG signal alone. Thus, signal processing (including feature extraction and classification) is a vital part of obtaining a good MI signal for robotic control. A general pipeline for EEG data processing involves several steps. First, the data undergo a series of pre-processing routines (e.g., filtering and artifact removal) before feature extraction and classification for use as a control signal for the robotic hand. There are variety of methods to remove artifact from EEG and these choices depend on the overall scope of the work . For instance, Independent Component Analysis (ICA) and Canonical Correlation Analysis (CCA) can support real-time applications but are dependent on manual input. In contrast, regression and wavelet methods are automated but support offline applications. There also exist automated and real-time applications such as adaptive filtering or using blind source separation (BSS) based methods. Recently, the research community has been pushing real-time artifact rejection by reducing computational complexity e.g. Enhanced Automatic Wavelet-ICA (EAWICA) , hybrid ICA—Wavelet transform technique (ICA-W)  or by developing new approaches such as adaptive de-noising frameworks  and Artifact Subspace Reconstruction (ASR) . Feature extraction involves recognising useful information (e.g., spectral power, time epochs, spatial filtering) for better discriminability among mental states. For example, the common spatial patterns (CSP) algorithm is a type of spatial filter that maximises the variance of band pass-filtered EEG from one class to discriminate it from another . Finally, classification (which can range from linear and simple algorithms such as Linear Discriminant Analysis (LDA), Linear Support Vector Machine (L-SVM) up to more complex techniques in deep learning such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) [31, 32] involves the translation of these signals of intent to an action that provides the user feedback and closes the loop of the motor intent-to-action circuit.
The potential of MI-based BCIs has gained considerable attraction because the neural activity involved in the control of the robotic device may be a key component in the rehabilitation itself. For example, MI of movement is thought to activate some of the neural networks involved in movement execution (ME) [33,34,35,36]. The resulting rationale is that encouraging the use of MI could increase the capacity of the motor cortex to control major muscle movements and decrease the necessity to use neural circuits damaged post-stroke. The scientific justification for this approach was first provided by Jeannerod  who suggested that the neural substrates of MI are part of a shared network that is also activated during the simulation of action by the observation of action (AO) . These ‘mirror neuron’ systems are thought to be an important component of motor control and learning —hence the belief that activating these systems could aid rehabilitation. The use of a MI-BCI to control a robot in comparison to traditional MI and physical practice provides a number of benefits to its user and the practitioner. These advantages include the fact that the former can provide a more streamlined approach such as sensing physiological states, automating visual and/or kinaesthetic feedback and enriching the task and increasing user motivation through gamification. There are also general concerns around the utility of motor imagery without physical movement (and the corresponding muscle development that comes from these) and it is possible that these issues could be overcome through a control strategy that progressively reduces the amount of support provided by the MI-BCI system and encourages active motor control [37, 38].
A recent meta-analysis of the neural correlates of action (MI, AO and ME) quantified ‘conjunct’ and ‘contrast’ networks in the cortical and subcortical regions . This analysis, which took advantage of open-source historical data from fMRI studies, reported consistent activation in the premotor, parietal and somatosensory areas for MI, AO and ME. Predicated on such data, researchers have reasoned that performing MI should cause activation of the neural substrates that are also involved in controlling movement and there have been a number of research projects that have used AO in combination with MI in neurorehabilitation [39,40,41] and motor learning studies [42, 43] over the last decade.
One implication of using MI and AO to justify the use of BCI approaches is that great care must be taken with regard to the quality of the environment in which the rehabilitation takes place. While people can learn to modulate their brain rhythms without using motor imagery and there is variability across individuals in their ability to imagine motor actions, MI-driven BCI systems require (by design at least) for patient to imagine a movement. Likewise, AO requires the patients to clearly see the action. This suggests that the richness and vividness of the visual cues provided is an essential part of an effective BCI system. It is also reasonable to assume that feedback is important within these processes and thus the quality of feedback should be considered as essential. Afterall, MI and AO are just tools to modulate brain states  and the effectiveness of these tools vary from one stroke patient to another . Finally, motivation is known to play an important role in promoting active participation during therapy [20, 45]. Thus, a good BCI system should incorporate an approach (such as gaming and positive reward) that increases motivation. Recent advances in technology make it far easier to create a rehabilitation environment that provides rich vivid cues, gives salient feedback and is motivating. For example, the rise of immersive technologies, including virtual reality (VR) and augmented reality (AR) platforms [45,46,47], allows for the creation of engaging visual experiences that have the potential to improve a patient’s self-efficacy  and thereby encourage the patient to maintain the rehabilitation regime. One specific example of this is visually amplifying the movement made by a patient when the movement is of limited extent so that the patient can see their efforts are producing results .
In this review we set out to examine the literature to achieve a better understanding of the current value and potential of BCI-based robotic therapy with three specific objectives:
- (1)Identify how BCI technologies are being utilised in controlling robotic devices for hand rehabilitation. Our focus was on the study design and the tasks that are employed in setting up a BCI-hand robot therapy protocol.
- (2)Document the readiness of BCI systems. Because BCI for rehabilitation is still an emerging field of research, we expected that most studies would be in their proof-of-concept or clinical testing stages of development. Our purpose was to determine the limits of this technology in terms of: (a) resolution of hand MI detection and (b) the degree of robotic control.
- (3)Evaluate the clinical significance of BCI-hand robot systems by looking at the outcome measures in motor recovery and determine if a standard protocol exists for these interventions.
It is important to note that there have been several recent reviews exploring BCI for stroke rehabilitation. For example, Monge-Pereira et al.  compiled EEG-based BCI studies for upper limb stroke rehabilitation. Their systematic review (involving 13 clinical studies on stroke and hemiplegic patients) reported on research methodological quality and improvements in the motor abilities of stroke patients. Cervera et al.  performed a meta-analysis on the clinical effectiveness of BCI-based stroke therapy among 9 randomised clinical trials (RCT). McConnell et al.  undertook a narrative review of 110 robotic devices with brain–machine interfaces for hand rehabilitation post-stroke. These reviews, in general, have reported that such systems provide improvements in both functional and clinical outcomes in pilot studies or trials involving small sample sizes. Thus, the literature indicates that EEG-based BCI are a promising general approach for rehabilitation post-stroke. The current work complements these previous reports by providing the first systematic examination on the use of BCI-robot systems for the rehabilitation of fine motor skills associated with hand movement and profiling these systems from a technical and clinical perspective.[…]
29 January 2021
At the North American Neuromodulation Society (NANS) virtual meeting (15–16 January), Dylan J Edwards, director of the Moss Rehabilitation Research Institute and director of Human Motor Recovery Laboratory (Philadelphia, USA), presented his proof-of-concept results for a study he led looking at transcranial electrical stimulation (TES) paired with robotic assisted therapies in stroke recovery.
Edwards noted that TES can be used as a monotherapy, but he is interested in its use in combination with other therapies (speech, occupational, physical, robotics, drugs). In this case the combination is physical therapy using robot-assisted therapies.
Edwards stated this current research was motivated by a 2010 clinical trial published in the New England Journal of Medicine which compared robot assisted therapy with intensive comparison therapy, over 12 weeks and 36 sessions. The primary end point of this study was the Fugl-Meyer Assessment, and according to Edwards it showed a, “small but significant improvement in motor function that was sustained after the 12 week intervention”.
Edwards commented, “Robot therapy is not the only method of performing intensive physical-type therapy, however, it makes sense because the robot does not tire, and the therapy is really structured and consistent, and that’s important when you’re looking to apply a supplementary therapy so that you have a stable behavioural therapy upon which to test the supplement.”
Edwards reports he carried out a study in 2009 investigating transcranial stimulation in chronic post stroke hemiparesis in a small group of subjects. They showed that anodal transcranial direct current stimulation (tDCS) of 2mA for 20 minutes could raise the excitability of the corticospinal tract. Edwards said this made them question if they could embark on a period of robotic training for a session lasting 45 to 60 minutes after the tDCS session and what would happen to that potential. From this Edwards claims they showed that the increase in corticospinal excitement could be sustained during that robot therapy. So these therapies could plausibly co-exist physiologically.
The hypothesis of Edwards current trial is that robot therapy and tDCS would lead to a greater improvement than robot therapy with sham tDCS on the upper extremity Fugl-Meyer (UEFM) scale. They also assessed motor function using the Wolf motor function test. Eighty-two patients with right hemiparesis completed the trial. Each subject had three sessions of tDCS a week for 12 weeks. Sessions lasted one hour and were accompanied by alternating shoulder, elbow, and wrist robotic training amounting to around one thousand repetitions. The primary endpoint was 12 weeks and there was a six month follow-up.
The subjects were randomised between the real and the sham stimulation, although participants wore the same electrodes and were all tDCS naïve.
Edwards explained how they carried out robot therapies. Patients place their affected limb into the robot handle which has a display monitor in front of it. Then using the robotic device, patients aim to hit certain targets represented on the display. Edwards noted that this can be used as either an assessment or a training tool.
While Edwards was able to point to other studies which showed positive results for TES and robot-assisted therapy, such as Allman et al, 2016, and Giacobbe et al, 2013, he reported that in the case of his study, “The tDCS did not confirm an advantage over sham stimulation in this context.”
However, he added, “We haven’t ruled out a potentially faster recovery trajectory in the tDCS group, and the results for that could not be answered by the design of this study. But that does warrant further exploration given that the two other studies I presented did have a shorter number of sessions, by about a third of what we did. If tCDS could indeed lead to fewer sessions being required for the same clinical benefit, that warrants further investigation.”