Posts Tagged hemiparesis

[Abstract + Related Articles] Combining Virtual Reality Motor Rehabilitation With Cognitive Strategy Use in Chronic Stroke

Abstract

Importance: Rehabilitation interventions for chronic stroke are largely impairment based, with results confined to the level of impairment instead of function. In contrast, cognitive strategy training interventions have demonstrated clinically meaningful improvements in functional outcomes. Integration of these approaches has yet to be explored.

Objective: To evaluate acceptability, recruitment, and retention rate and determine which outcome measures best capture the effect of the intervention.

Design: Single-group, pre–post design.

Setting: Research laboratory.

Participants: Adults with chronic stroke and hemiparesis (N = 10).

Intervention: A 12-wk intervention integrating cognitive strategy training with upper extremity motor training. Two weekly sessions used Kinect-based virtual reality to encourage high numbers of upper extremity movement repetitions. The third weekly session focused on the use of cognitive strategies with practice of client-centered goals.

Outcomes and Measures: Upper extremity motor performance was measured with the Fugl–Meyer Assessment. Occupational performance on trained and untrained goals was measured via the Performance Quality Rating Scale and the Canadian Occupational Performance Measure. Outcome data were gathered preintervention, postintervention, and at 3-mo follow-up.

Results: The intervention was perceived as acceptable. Recruitment rate was 15%, and retention rate was 100%. Large effects were found on outcomes of upper extremity motor performance, occupational performance, and participation at follow-up.

Conclusion and Relevance: MetacogVR is feasible for adults with chronic stroke. The effect of MetacogVR is best captured through measures of upper extremity motor performance, occupational performance, and participation.

What This Articles Adds: Traditional, impairment-based approaches to chronic stroke rehabilitation may require integration with cognitive-strategy training to affect performance on meaningful goals.

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via Combining Virtual Reality Motor Rehabilitation With Cognitive Strategy Use in Chronic Stroke | American Journal of Occupational Therapy

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[Abstract + References] Development of a Hand Rehabilitation Therapy System with Soft Robotic Glove – Conference paper

Abstract

The major cause of problems with hand motility in adults is due to work accidents, strokes, injuries and work accidents. The emergence of robotic gloves for hand rehabilitation therapy has been developed to assist with rehabilitation treatment. In this scientific paper, a robotic glove prosthesis is designed and developed for use in hand rehabilitation in patients with grip pathologies. There is talk of mechanical design and operation, and the glove is controlled by a mobile application that allows the physiotherapist to enter the settings for the patient or allow an expert system based on 15 rules to do so. The system is capable of generating reports for the patient, the physiotherapist or the caregiver to review. The developed system is portable, lightweight and easy to transport. The validation of the prototype was carried out with adult patients suffering from hemiparesis.

References

  1. 1.
    Borboni, A., Mor, M., Fgila, R.: Gloreha—hand robotic rehabilitation: design, mechanical model, and experiments (2016). asmedigitalcollection.asme.orgCrossRefGoogle Scholar
  2. 2.
    Henao, G., Arango, J., Rendon, J.F.G., Velasquez, J.B., Tellez, C.H.O.: Rehabilitación temprana de fracturas de mano con órtesis robóticas. Reporte de caso. Elsevier (2018)Google Scholar
  3. 3.
    Chu, C.-Y., Patterson, R.M.: Soft robotic devices for hand rehabilitation and assistance: a narrative review. J. Neuroeng. Rehabil. 15(1), 9 (2018)CrossRefGoogle Scholar
  4. 4.
    Polygerinos, P., Galloway, K.C., Savage, E., Herman, M., O’Donnell, K., Walsh, C.J.: Soft robotic glove for hand rehabilitation and task specific training (2015). ieeexplore.ieee.org
  5. 5.
    Ueki, S., Kawasaki, H., Ito, S., Nishimoto, Y., Abe, M., Aoki, T., Ishigure, Y., Ojika, T., Mouri, T.: Development of a hand-assist robot with multi-degrees-of-freedom for rehabilitation therapy (2012). ieeexplore.ieee.orgCrossRefGoogle Scholar
  6. 6.
    Kutner, N., Zhang, R., Butler, A.J., Wolf, S.L., Alberts, J.L.: Quality-of-life change associated with robotic-assisted therapy to improve hand motor function in patients with subacute stroke: a randomized clinical trial (2010). academic.oup.com
  7. 7.
    Abdallah, I.B., Bouteraa, Y., Rekik, C.: Design and development of 3D printed myoelectric robotic exoskeleton for hand rehabilitation. Int. J. Smart Sens. Intell. Syst. 10(2), 341–366 (2017)Google Scholar
  8. 8.
    Cempini, M., Cortese, M., Vitiello, A.: A powered finger–thumb wearable hand exoskeleton with self-aligning joint axes (2015). ieeexplore.ieee.org
  9. 9.
    Heo, P., Gu, G.M., Lee, S., Rhee, K., Kim, J.: Current hand exoskeleton technologies for rehabilitation and assistive engineering. Int. J. Precis. Eng. Manuf. 13(5), 807–824 (2012)CrossRefGoogle Scholar
  10. 10.
    Jones, C., Wang, F., Morrison, R., Sarkar, N., Kamper, D.G.: Design and development of the cable actuated finger exoskeleton for hand rehabilitation following stroke (2014). ieeexplore.ieee.orgCrossRefGoogle Scholar
  11. 11.
    Polygerinos, P., Wang, Z., Galloway, K., Wood, R.J., Walsh, C.J.: Soft robotic glove for combined assistance and at-home rehabilitation. Elsevier (2015)Google Scholar
  12. 12.
    Maciejasz, P., Eschweiler, J., Gerlach-Hahn, K., Jansen-Troy, A., Leonhardt, S.: A survey on robotic devices for upper limb rehabilitation. J. Neuroeng. Rehabil. 11(1), 3 (2014)CrossRefGoogle Scholar
  13. 13.
    Avila Chaurand, R., Prado León, L.R., González Muñoz, E.L., and Universidad de Guadalajara: Centro de Investigaciones en Ergonomía., Dimensiones antropométricas de población latinoamericana. Universidad de Guadalajara, Centro Universitario de Arte, Arquitectura y Diseño, División de Tecnología y Procesos, Departamento de Producción y Desarrollo, Centro de Investigaciones en Ergonomía (2001)Google Scholar

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[WEB SITE] Flint Rehab Introduces MiGo Wearable for Stroke Recovery

MiGo

Flint Rehab announces the launch of MiGo, a wearable activity tracker specifically designed for stroke survivors. The device makes its official debut at the 2019 Consumer Electronics Show in Las Vegas.

MiGo is designed to track upper extremity activity — in addition to walking — and is optimized for the movement patterns performed by individuals with stroke. The device is accompanied by a smartphone app that provides motivational support through digital coaching, progressive goal setting, and social networking with other stroke survivors, according to the company in a media release.

“Most wearable fitness trackers are designed to help people get into shape. MiGo is a new type of wearable that helps people regain their independence after a stroke,” says Dr Nizan Friedman, co-founder and CEO of Irvine, Calif-based Flint Rehab, in the release.

“Traditionally, innovation in medical technology has been limited by what insurance companies are willing to cover. As a consumer-level digital health technology, MiGo avoids these constraints, empowering stroke survivors to take their recovery into their own hands.”

A common outcome of stroke is hemiparesis, or impaired movement on one side of the body. One of the leading causes of this lifelong disability is a phenomenon called “learned non-use,” where stroke survivors neglect to use their impaired arm or leg, causing their brain to lose the ability to control those limbs altogether.

MiGo directly addresses the problem of learned non-use by motivating stroke survivors to use their impaired side as much as possible. Using deep-learning algorithms, MiGo accurately tracks how much the wearer is using their impaired side, providing them with an easy-to-understand rep count throughout the day.

MiGo also provides an intelligent activity goal that updates every day based on the wearer’s actual movement ability, ensuring every user stays continuously challenged at the level appropriate for them. Then, the device acts as the wearer’s personal cheerleader, giving them rewards and positive feedback right on their wrist as they work to hit their daily goal, the release explains.

“Suffering a stroke is a traumatic, life-changing event. Many survivors do not have the proper support network to deal with the event, and they may find it difficult to relate with friends and family who don’t understand what they are going through,” states Dan Zondervan, co-founder and vice president of Flint Rehab.

“Using the MiGo app, users can join groups to share their activity data and collaborate with other stroke survivors to achieve group goals. Group members can also share their experiences and offer encouraging support to each other — right in the app,” he adds.

For more information, visit Flint Rehab.

[Source(s0): Flint Rehab, Business Wire]

 

via Flint Rehab Introduces MiGo Wearable for Stroke Recovery – Rehab Managment

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[Abstract] A novel neurocognitive rehabilitation tool in the recovery of hemiplegic hand grip after stroke: a case report.

Abstract

Stroke has significant physical, psychological and social consequences. Recent rehabilitation approaches suggest that cognitive exercises with dual-task (sensory-motor) exercises positively influence the recovery and function of the hemiplegic hand grip. The purpose of this study was to describe a rehabilitation protocol involving the use of a new neurocognitive tool called “UOVO” for hand grip recovery after stroke. A 58-year-old right-handed male patient in the chronic stage of stroke, presenting with left-sided hemiparesis and marked motor deficits at the level of the left hand and forearm, was treated with the UOVO, a new rehabilitation instrument based on the neurocognitive rehabilitation theory of Perfetti. The patient was evaluated at T0 (before treatment), T1 (after treatment) and T2 (2 months of follow-up). At T2, the patient showed improvements of motor functions, shoulder, elbow and wrist spasticity, motility and performance. This case report explores the possibility of improving traditional rehabilitation through a neurocognitive approach with a dual-task paradigm (including motor and somato-sensory stimulation), specifically one involving the use of an original rehabilitation aid named UOVO, which lends itself very well to exercises proposed through the use of motor imagery. The results were encouraging and showed improvements in hemiplegic hand grip function and recovery. However, further studies, in the form of randomized controlled trials, will be needed to further explore and confirm our results.

 

via A novel neurocognitive rehabilitation tool in the recovery of hemiplegic hand grip after stroke: a case report. – PubMed – NCBI

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[ARTICLE] Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control – Full Text

Abstract

Background

Ankle exoskeletons offer a promising opportunity to offset mechanical deficits after stroke by applying the needed torque at the paretic ankle. Because joint torque is related to gait speed, it is important to consider the user’s gait speed when determining the magnitude of assistive joint torque. We developed and tested a novel exoskeleton controller for delivering propulsive assistance which modulates exoskeleton torque magnitude based on both soleus muscle activity and walking speed. The purpose of this research is to assess the impact of the resulting exoskeleton assistance on post-stroke walking performance across a range of walking speeds.

Methods

Six participants with stroke walked with and without assistance applied to a powered ankle exoskeleton on the paretic limb. Walking speed started at 60% of their comfortable overground speed and was increased each minute (n00, n01, n02, etc.). We measured lower limb joint and limb powers, metabolic cost of transport, paretic and non-paretic limb propulsion, and trailing limb angle.

Results

Exoskeleton assistance increased with walking speed, verifying the speed-adaptive nature of the controller. Both paretic ankle joint power and total limb power increased significantly with exoskeleton assistance at six walking speeds (n00, n01, n02, n03, n04, n05). Despite these joint- and limb-level benefits associated with exoskeleton assistance, no subject averaged metabolic benefits were evident when compared to the unassisted condition. Both paretic trailing limb angle and integrated anterior paretic ground reaction forces were reduced with assistance applied as compared to no assistance at four speeds (n00, n01, n02, n03).

Conclusions

Our results suggest that despite appropriate scaling of ankle assistance by the exoskeleton controller, suboptimal limb posture limited the conversion of exoskeleton assistance into forward propulsion. Future studies could include biofeedback or verbal cues to guide users into limb configurations that encourage the conversion of mechanical power at the ankle to forward propulsion.

Trial registration

N/A.

Background

Walking after a stroke is more metabolically expensive, leading to rapid exhaustion, limited mobility, and reduced physical activity [1]. Hemiparetic walking is slow and asymmetric compared to unimpaired gait. Preferred walking speeds following stroke range between < 0.2 m s− 1 and ~ 0.8 m s− 1 [2] compared to ~ 1.4 m s− 1 in unimpaired adults, and large interlimb asymmetry has been documented in ankle joint power output [34]. The ankle plantarflexors are responsible for up to 50% of the total positive work needed to maintain forward gait [56]; therefore, weakness of the paretic plantarflexors is especially debilitating, and as a result, the paretic ankle is often a specific target of stroke rehabilitation [78910]. In recent years, ankle exoskeletons have emerged as a technology capable of improving ankle power output by applying torque at the ankle joint during walking in clinical populations [78] and healthy controls [11121314]. Myoelectric exoskeletons offer a user-controlled approach to stroke rehabilitation by measuring and adapting to changes in the user’s soleus electromyography (EMG) when generating torque profiles applied at the ankle [15]. For example, a proportional myoelectric ankle exoskeleton was shown to increase the paretic plantarflexion moment for persons post-stroke walking at 75% of their comfortable overground (OVG) speed [8]; despite these improvements, assistance did not reduce the metabolic cost of walking or improve percent paretic propulsion. The authors suggested exoskeleton performance could be limited because the walking speed was restricted to a pace at which exoskeleton assistance was not needed.

Exoskeleton design for improved function following a stroke would benefit from understanding the interaction among exoskeleton assistance, changes in walking speed, and measured walking performance. Increases in walking speed post-stroke are associated with improvements in forward propulsion and propulsion symmetry [16], trailing limb posture [1718], step length symmetries [1719], and greater walking economies [1719]. This suggests that assistive technologies need to account for variability in walking speeds to further improve post-stroke walking outcomes. However, research to date has evaluated exoskeleton performance at only one walking speed, typically set to either the participant’s comfortable OVG speed or a speed below this value [78]. At constant speeds, ankle exoskeletons have been shown to improve total ankle power in both healthy controls [11] and persons post-stroke [8], suggesting the joint powers and joint power symmetries could be improved by exoskeleton technology. Additionally, an exosuit applying assistance to the ankle was able to improve paretic propulsion and metabolic cost in persons post-stroke walking at their comfortable OVG speed [7]. Assessing the impact of exoskeleton assistance on walking performance across a range of speeds is the next logical step toward developing exoskeleton intervention strategies targeted at improving walking performance and quality of life for millions of persons post-stroke.

In order to assess the impact of exoskeleton assistance across a range of walking speeds in persons post-stroke, we developed a novel, speed-adaptive exoskeleton controller that automatically modulates the magnitude of ankle torque with changes in walking speed and soleus EMG. We hypothesized that: 1) Our novel speed-adaptive controller will scale exoskeleton assistance with increases in walking speed as intended. 2) Exoskeleton assistance will lead to increases in total average net paretic ankle power and limb power at all walking speeds. 3) Exoskeleton assistance will lead to metabolic benefits associated with improved paretic average net ankle and limb powers.

Methods

Exoskeleton hardware

We implemented an exoskeleton emulator comprised of a powerful off-board actuation and control system, a flexible Bowden cable transmission, and a lightweight exoskeleton end effector [20]. The exoskeleton end effector includes shank and foot carbon fiber components custom fitted to participants and hinged at the ankle. The desired exoskeleton torque profile was applied by a benchtop motor (Baldor Electric Co, USA) to the carbon-fiber ankle exoskeleton through a Bowden-cable transmission system. An inline tensile load cell (DCE-2500 N, LCM Systems, Newport, UK) was used to confirm the force transmitted by the exoskeleton emulator during exoskeleton assistance.

Speed-adaptive proportional myoelectric exoskeleton controller

Our exoskeleton controller alters the timing and magnitude of assistance with the user’s soleus EMG signal and walking speed (Fig. 1). The exoskeleton torque is determined from Eq. 1, in which participant mass (mparticipant) is constant across speeds, treadmill speed (V) is measured in real-time, the speed gain (Gspeed) is constant for all subjects and across speeds, the adaptive gain (Gadp) is constant for a gait cycle and calculated anew for each gait cycle, and the force-gated and normalized EMG (EMGGRFgated) is a continuously changing variable.

τexo (t)=mparticipant×V×Gspeed×Gadp×EMGGRFgatedτexo (t)=mparticipant×V×Gspeed×Gadp×EMGGRFgated
(1)
Fig. 1
Fig. 1

Novel speed-adaptive myoelectric exoskeleton controller measures and adapts to users’ soleus EMG signal as well as their walking speed in order to generate the exoskeleton torque profile. Raw soleus EMG signal is filtered and rectified to create an EMG envelope, and the created EMG envelope is then gated by anterior GRFs to ensure assistance is only applied during forward propulsion. The adaptive EMG gain is calculated as a moving average of peak force-gated EMG from the last five paretic gait cycles. The pre-speed gain control signal is the product of the force-gated EMG and the adaptive EMG gain. The speed gain is determined using real-time walking speed and computed as 25% of the maximum biological plantarflexion torque at that given walking speed. Exoskeleton torque is the result of multiplying the speed gain with the pre-speed gain control signal

[…]

Continue —> Mechanics and energetics of post-stroke walking aided by a powered ankle exoskeleton with speed-adaptive myoelectric control | Journal of NeuroEngineering and Rehabilitation | Full Text

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[Abstract] Effectiveness of Virtual Reality Using PS4 Gaming Technology in Stroke Rehabilitation for Improving Upper Limb Function-A Pilot Study

Background: Hemiparesis resulting in functional limitation of an upper extremity and lower limb is common among stroke survivors. Virtual reality is one of the way of improving motor function in stroke, limited evidence is available on the efficacy of virtual reality for stroke rehabilitaton.

Methods: In this pilot study 2 parallel groups involving stroke patients, we compared the feasibility, safety and efficacy of virtual reality using the sony PS4 gaming technology to evaluate upper limb motor improvement. The primary feasibility outcome was the total time receiving the intervention. The… primary safety outcome was the proportion of patients experiencing intervention-related adverse events during the study period. Efficacy, a secondary outcome measure, was evaluated with wolf motor function test and Spasticity Grading at 4 weeks after intervention. OUTCOME MEASURE: WOLF Motor function test and Box and Block test.

Result: This study shows that mean values obtained from WOLF motor function test showed no statistical significance and the mean values of Box and Block test showed statistical significance.

Conclusion: This study concludes that the PS4 gaming technology is a feasible, safe, and potentially effective intervention to enhance motor function recovery in patients with a recent stroke.

Indian Journals

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[REVIEW ARTICLE] Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach – Full Text

Robot-mediated therapy is an innovative form of rehabilitation that enables highly repetitive, intensive, adaptive, and quantifiable physical training. It has been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis. Multiple studies collated in a growing number of review articles showed the positive effects on motor impairment, less clearly on functional limitations. After describing the current status of robotic therapy after upper limb paresis due to stroke, this overview addresses basic principles related to robotic therapy applied to upper limb paresis. We demonstrate how this innovation is an evidence-based approach in that it meets both the improved clinical and more fundamental knowledge-base about regaining effective motor function after stroke and the need of more objective, flexible and controlled therapeutic paradigms.

Introduction

Robot-mediated rehabilitation is an innovative exercise-based therapy using robotic devices that enable the implementation of highly repetitive, intensive, adaptive, and quantifiable physical training. Since the first clinical studies with the MIT-Manus robot (1), robotic applications have been increasingly used to restore loss of motor function, mainly in stroke survivors suffering from an upper limb paresis but also in cerebral palsy (2), multiple sclerosis (3), spinal cord injury (4), and other disease types. Thus, multiple studies suggested that robot-assisted training, integrated into a multidisciplinary program, resulted in an additional reduction of motor impairments in comparison to usual care alone in different stages of stroke recovery: namely, acute (57), subacute (18), and chronic phases after the stroke onset (911). Typically, patients engaged in the robotic therapy showed an impairment reduction of 5 points or more in the Fugl-Meyer assessment as compared to usual care. Of notice, rehabilitation studies conducted during the chronic stroke phase suggest that a 5-point differential represents the minimum clinically important difference (MCID), i.e., the magnitude of change that is necessary to produce real-world benefits for patients (12). These results were collated in multiple review articles and meta-analyses (1317). In contrast, the advantage of robotic training over usual care in terms of functional benefit is less clear, but there are recent results that suggest how best to organize training to achieve superior results in terms of both impairment and function (18). Indeed, the use of the robotic tool has allowed us the parse and study the ingredients that should form an efficacious and efficient rehabilitation program. The aim of this paper is to provide a general overview of the current state of robotic training in upper limb rehabilitation after stroke, to analyze the rationale behind its use, and to discuss our working model on how to more effectively employ robotics to promote motor recovery after stroke.

Upper Extremity Robotic Therapy: Current Status

Robotic systems used in the field of neurorehabilitation can be organized under two basic categories: exoskeleton and end-effector type robots. Exoskeleton robotic systems allow us to accurately determine the kinematic configuration of human joints, while end-effector type robots exert forces only in the most distal part of the affected limb. A growing number of commercial robotic devices have been developed employing either configuration. Examples of exoskeleton type include the Armeo®Spring, Armeo®Power, and Myomo® and of end-effector type include the InMotion™, Burt®, Kinarm™ and REAplan®. Both categories enable the implementation of intensive training and there are many other devices in different stages of development or commercialization (1920).

The last decade has seen an exponential growth in both the number of devices as well as clinical trials. The results coalesced in a set of systematic reviews, meta-analyses (1317) and guidelines such as those published by the American Heart Association and the Veterans Administration (AHA and VA) (21). There is a clear consensus that upper limb therapy using robotic devices over 30–60-min sessions, is safe despite the larger number of movement repetitions (14).

This technic is feasible and showed a high rate of eligibility; in the VA ROBOTICS (911) study, nearly two thirds of interviewed stroke survivors were enrolled in the study. As a comparison the EXCITE cohort of constraint-induced movement therapy enrolled only 6% of the screened patients participated (22). On that issue, it is relevant to notice the admission criteria of both chronic stroke studies. ROBOTICS enrolled subjects with Fugl-Meyer assessment (FMA) of 38 or lower (out of 66) while EXCITE typically enrolled subjects with an FMA of 42 or higher. Duret and colleagues demonstrated that the target population, based on motor impairments, seems to be broader in the robotic intervention which includes patients with severe motor impairments, a group that typically has not seen much benefit from usual care (23). Indeed, Duret found that more severely impaired patients benefited more from robot-assisted training and that co-factors such as age, aphasia, and neglect had no impact on the amount of repetitive movements performed and were not contraindicated. Furthermore, all patients enrolled in robotic training were satisfied with the intervention. This result is consistent with the literature (24).

The main outcome result is that robotic therapy led to significantly more improvement in impairment as compared to conventional usual care, but only slightly more on motor function of the limb segments targeted by the robotic device (16). For example, Bertani et al. (15) and Zhang et al. (17) found that robotic training was more effective in reducing motor impairment than conventional usual care therapy in patients with chronic stroke, and further meta-analyses suggested that using robotic therapy as an adjunct to conventional usual care treatment is more effective than robotic training alone (1317). Other examples of disproven beliefs: many rehabilitation professionals mistakenly expected significant increase of muscle hyperactivity and shoulder pain due to the intensive training. Most studies showed just the opposite, i.e., that intensive robotic training was associated with tone reduction as compared to the usual care groups (92526). These results are shattering the resistance to the widespread adoption of robotic therapy as a therapeutic modality post-stroke.

That said, not all is rosy. Superior changes in functional outcomes were more controversial until the very last years as most studies and reviews concluded that robotic therapy did not improve activities of daily living beyond traditional care. One first step was reached in 2015 with Mehrholz et al. (14), who found that robotic therapy can provide more functional benefits when compared to other interventions however with a quality of evidence low to very low. 2018 may have seen a decisive step in favor of robotic as the latest meta-analysis conducted by Mehrholz et al. (27) concluded that robot-assisted arm training may improve activities of daily living in the acute phase after stroke with a high quality of evidence However, the results must be interpreted with caution because of the high variability in trial designs as evidenced by the multicenter study (28) in which robotic rehabilitation using the Armeo®Spring, a non-motorized device, was compared to self-management with negative results on motor impairments and potential functional benefits in the robotic group.

The Robot Assisted Training for the Upper Limb after Stroke (RATULS) study (29) might clarify things and put everyone in agreement on the topic. Of notice, RATULS goes beyond the Veterans Administration ROBOTICS with chronic stroke or the French REM_AVC study with subacute stroke. RATULS included 770 stroke patients and covered all stroke phases, from acute to chronic, and it included a positive meaningful control in addition to usual care.[…]

 

Continue —->  Frontiers | Robot-Assisted Therapy in Upper Extremity Hemiparesis: Overview of an Evidence-Based Approach | Neurology

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[ARTICLE] Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study – Full Text

Abstract

Background

Intensive robot-assisted training of the upper limb after stroke can reduce motor impairment, even at the chronic stage. However, the effectiveness of practice for recovery depends on the selection of the practised movements. We hypothesized that rehabilitation can be optimized by selecting the movements to be practiced based on the trainee’s performance profile.

Methods

We present a novel principle (‘steepest gradients’) for performance-based selection of movements. The principle is based on mapping motor performance across a workspace and then selecting movements located at regions of the steepest transition between better and worse performance.

To assess the benefit of this principle we compared the effect of 15 sessions of robot-assisted reaching training on upper-limb motor impairment, between two groups of people who have moderate-to-severe chronic upper-limb hemiparesis due to stroke. The test group (N = 7) received steepest gradients-based training, iteratively selected according to the steepest gradients principle with weekly remapping, whereas the control group (N = 9) received a standard “centre-out” reaching training. Training intensity was identical.

Results

Both groups showed improvement in Fugl-Meyer upper-extremity scores (the primary outcome measure). Moreover, the test group showed significantly greater improvement (twofold) compared to control. The score remained elevated, on average, for at least 4 weeks although the additional benefit of the steepest-gradients -based training diminished relative to control.

Conclusions

This study provides a proof of concept for the superior benefit of performance-based selection of practiced movements in reducing upper-limb motor impairment due to stroke. This added benefit was most evident in the short term, suggesting that performance-based steepest-gradients training may be effective in increasing the rate of initial phase of practice-based recovery; we discuss how long-term retention may also be improved.

Background

Upper-limb (UL) motor impairment is a common outcome of stroke that can severely hamper basic daily living activities [123]. Training-based therapy can promote recovery with the outcome depending on the intensity and duration of the intervention [456]. Robot-assisted training allows intense practice without increasing the individual’s dependence on a therapist and can improve clinical scores of UL motor capacity [789]. However, the effects are usually small and provide limited improvement in motor function, especially in more severe hemiparesis [67101112]. Identifying training methods that can boost outcome is thus vital. Considering the extent of effort and sophistication invested in robot-assisted technology (e.g. [1314]) perhaps it is time to focus on how to optimise its utility (in terms of training principles). Recent attempts have focussed on creating training scenarios which are more engaging or which simulate daily living activities. However, the evidence for the added benefit of this approach is mixed [15]. Another approach is to individualize the difficulty of the practised task (e.g. [1617]). This is based on the idea that motor improvement depends on the ability to ‘make sense’ of information related to performance [18], and postulates that matching the challenge (difficulty) level of the training task to the current ability of the trainee would optimise motor learning [19]. Individualizing task difficulty is commonly achieved by adjusting the parameters controlling task demands (e.g. movement speed or distance; or amount of assistance) across a pre-selected standard set of movements, to match the ability of the individual. Yet, so far there is little evidence for the added benefit of this approach for UL motor recovery. Hence, individually adjusting the task difficulty level might –by itself – not suffice for boosting UL rehabilitation outcome.

We hypothesised instead that appropriate selection of the practiced movements – in terms of the muscle coordination patterns – is a key for improving motor recovery. UL hemiparesis can affect various aspects of control. Thus, different motor impairments may benefit from different training movements. For example, training with movements involving mainly patterns of intact muscle coordination is unlikely to contribute much to improve other impaired movement patterns, regardless of the task difficulty level. Similarly, training that focuses only on movements that involve severely impaired muscle control may contribute little, even if the task can be performed by compensatory movements. Hence, to be optimally effective, individualized training may need to be expressed, not only by individually adjusting the level of difficulty of the task, but also in selecting tasks which are relevant for recovery. Little has been done to explore this possibility (for some attempts see [2021]). Here we present a novel method for performance-based selection of the set of movement tasks for robot-assisted training. The method depends on the availability of a motor performance “map” that profiles performance across a workspace. Movements are selected within intermediate levels of performance, based on the variation of performance across the map. Specifically, we predicted that optimal reduction of UL hemiparesis would be achieved by training with movements located at points on the map of steep transition (steep gradient) from high to low performance (Fig. 1), thus promoting the cascade of generalisation of motor improvement. Improved performance of movements at these steep gradient locations on the performance map would steer improvement in neighbouring, but more impaired regions, and encourage recovery. Here, we present evidence supporting this hypothesis.

Fig. 1

Fig. 1Illustrative sketch of the principle of selection of trained movements, based on the steepest gradients in a hypothetical motor performance profile (e.g. reaching aiming; vertical axis) measured across some particular task parameter (e.g. movement direction; horizontal axis); for simplicity, we show here a single dimension. The selected movements (grey horizontal bars) correspond to the regions with the steepest performance gradients, indicated by dashed ellipses. This movement selection principle can be applied where movement tasks can be defined by one or more continuous parameters, i.e. in a 1D, 2D, or higher dimensional map as long as the derivative of performance can be calculated. In this study we applied this principle on two measures of reaching performance (ability to move and ability to aim) each measured across two dimensions of the task (target location and movement direction)

To apply our method we first developed a novel principle of mapping of robot-assisted reaching performance across two dimensions of target location and movement direction [22], informing us about postural and movement-related aspects of motor control, respectively—key factors in the planning and execution of reaching movements [232425]. The performance maps then served to select movement sets for training, based on our “steepest gradients” principle. To test our hypothesis–namely, training based on that principle would lead to superior recovery–we compared the outcome of 15 sessions of robot-assisted training between two groups of people who have severe-to-moderate chronic UL hemiparesis due to stroke, differing only in the selection of trained movement. In one group the selection was based on the steepest performance gradients principle (updated weekly) whereas the other group was trained with a fixed set of centre-out reaching movements regardless of participant’s performance profile, as commonly used in robot-assisted UL therapy [26].[…]

 

Continue —-> Boosting robot-assisted rehabilitation of stroke hemiparesis by individualized selection of upper limb movements – a pilot study | Journal of NeuroEngineering and Rehabilitation | Full Text

Fig. 2Experimental design. a. The sessions in each of the 3 participation phases are shown, with different colours indicating different session type. CA: clinical assessment; Map: mapping session. The first CA also served for screening. b. Schematic description of the experimental setting (top view; adapted from [32]). The participant held the robot handle, with grip ensured by a glove (Active Hands Co Ltd) and arm supported against gravity (SaeboMass, Saebo Inc.; not shown), which—at the beginning of each trial – was gently moved by the robot to a start position (white on-screen disc). Next, a target appeared on the horizontal display (blue on-screen disc; here shown black) and the participant tried to reach the target within the allotted time as accurately as possible, with the robot providing assisting and guiding forces as needed at each moment. Hand position was indicated on-screen by a red disc (not shown here). The horizontal display occluded the hand and the manipulandum from vision. Participants wore a harness to restrict trunk movement, keeping their forehead on a padded headrest attached to the workstation frame. The assistive force (Assist) promoted slower-than-allowed movements and also impeded very fast rebound-like movements characterising high elbow flexor muscle tone. The guiding force (Guide) impeded lateral deviation from a straight path towards the target. An animated ‘explosion’ was presented at the end of each trial with its final radius indicating reach accuracy (not shown). Also, during training sessions a 4-bar histogram summary, shown after each block (84 trials), informed the participant about his or her ability to initiate movements, move, aim and reach the target (adopted from [16]). c. The reaching workspace used for mapping performance. The locations of the 8 targets are indicated by small open circles and are specified by angular coordinates relative to the centre. An example of the hand located at the 90otarget is shown. Participants made 5 cm reaches to each target from 8 start locations (indicated, for the example target, by small black dots and arrows), which were also specified in angular coordinates relative to the particular target. Note that the start coordinates therefore correspond to intended movement direction. The dashed circle indicates the extent of the mapped workspace, centred 24 cm in front of the headrest

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[ARTICLE] Effects of Hand Configuration on the Grasping, Holding, and Placement of an Instrumented Object in Patients With Hemiparesis – Full Text

Abstract

Objective: Limitations with manual dexterity are an important problem for patients suffering from hemiparesis post stroke. Sensorimotor deficits, compensatory strategies and the use of alternative grasping configurations may influence the efficiency of prehensile motor behavior. The aim of the present study is to examine how different grasp configurations affect patient ability to regulate both grip forces and object orientation when lifting, holding and placing an object.

Methods: Twelve stroke patients with mild to moderate hemiparesis were recruited. Each was required to lift, hold and replace an instrumented object. Four different grasp configurations were tested on both the hemiparetic and less affected arms. Load cells from each of the 6 faces of the instrumented object and an integrated inertial measurement unit were used to extract data regarding the timing of unloading/loading phases, regulation of grip forces, and object orientation throughout the task.

Results: Grip forces were greatest when using a palmar-digital grasp and lowest when using a top grasp. The time delay between peak acceleration and maximum grip force was also greatest for palmar-digital grasp and lowest for the top grasp. Use of the hemiparetic arm was associated with increased duration of the unloading phase and greater difficulty with maintaining the vertical orientation of the object at the transitions to object lifting and object placement. The occurrence of touch and push errors at the onset of grasp varied according to both grasp configuration and use of the hemiparetic arm.

Conclusion: Stroke patients exhibit impairments in the scale and temporal precision of grip force adjustments and reduced ability to maintain object orientation with various grasp configurations using the hemiparetic arm. Nonetheless, the timing and magnitude of grip force adjustments may be facilitated using a top grasp configuration. Conversely, whole hand prehension strategies compound difficulties with grip force scaling and inhibit the synchrony of grasp onset and object release.

 

Introduction

Cerebrovascular accidents (stroke) are a frequent cause of disability (1) and the recovery of upper-limb function in particular, is a key determinant of independence in activities of daily living (2). Broadly speaking, the physical impairment experienced by patients is characterized by loss of strength, abnormal movement patterns (pathological synergies), and changes in muscle tone to the side of the body contralateral to the stroke (34). This presentation is commonly referred to as hemiparesis and its severity tends to reflect the extent of the lesion to the corticospinal tract (5). Subtle changes in movement kinematics and hand function on the ipsilesional upper-limb have also been documented and may be the consequence of direct impairment of ipsilateral motor pathways (67), as well as reorganization of the non-lesioned hemisphere to support recovery of motor-function in the hemiparetic limb (810). Above all though, patients living with stroke find that limitations with manual dexterity of the hemiparetic arm have the most significant effect upon their ability to carry out activities involving hand use in daily life (11).

These impairments in patient hand function manifest in multiple different aspects of motor performance. This may include reduced strength (3), loss of individuated finger control (12), and abnormal force control at the level of the fingers (13). Increased muscle tone and spasticity though the flexors of the wrist and hand may further compound these difficulties and inhibit the ability to open the hand in preparation for grasping (14). Atypical reaching and grasping patterns are often seen to emerge both as a consequence of and as a response to the motor dysfunction (1516).

Unfortunately, rehabilitation of upper limb impairments proves to be challenging. Whilst numerous therapeutic modalities (e.g., bilateral training, constraint-induced therapy, electrical stimulation, task-oriented, high intensity programs) have been evaluated in clinical trials, none have demonstrated consistent effects upon hand function (1719). Indeed, previous research papers have described therapy outcomes in upper limb rehabilitation post stroke as “unacceptably poor” (20). Ideally, the design of neurorehabilitation programs should be grounded upon an understanding of basic mechanisms involved in neural plasticity and motor learning (2122). Part of this process implies coming to terms with the factors which characterize the disorganization in voluntary motor output (21). However, the majority of clinical tools currently used for evaluating hand function distinguish motor performance according to ordinal rating scales or task completion time (e.g., Frenchay Arm Test, Jebson-Taylor Hand Function Test) (2324). These kinds of assessments lack sensitivity and may prove insufficient for detecting the presence of mild motor deficits or subtle, yet clinically important changes in hand coordination (2526). Evidence based frameworks for hand rehabilitation have specifically called for the integration of new technology to support patient assessment and treatment planning (27). Despite this, the transposition of technology for upper limb rehabilitation from the research domain into clinical practice has been limited (2829). In the assessment of manual dexterity, the underlying challenge involves analyzing sensorimotor function of the hand with respect to its interaction with objects in the environment (30).

Successfully managing grasping and object handling tasks requires skilled control of prehensile finger forces. In healthy adults, grip forces are regulated to be marginally greater than the minimum required to prevent the object from slipping (31). This safety margin is calibrated according to the shape, surface friction and weight distribution of the object (3233). As the hand moves through space (lifting, transporting, object placement), grip force is continually modulated, proportional to the load forces associated with the mass and acceleration of that object (34). This temporal coupling between grip and load forces is considered a hallmark of anticipatory sensorimotor control (35). Disruption to motor planning, volitional motor control or somatosensory feedback may lead to a breakdown in the timing and magnitude of grip force adjustments.

Numerous studies have examined grip force regulation in neurological pathologies including cerebellar dysfunction (36), peripheral sensory neuropathy (3738), Parkinson’s disease (36373940) as well as congenital and acquired brain lesions (13364145). For patients suffering from hemiparesis post stroke, difficulty with coordinating the grasping and lifting action are frequently associated with temporal discrepancies between grip forces and load forces (46). The cerebral hemisphere implicated in the CVA (1347) and the extent of the resulting sensory deficits (4849) have also been observed to influence anticipatory grip force scaling. This body of work highlights the potential interest of using instrumented objects for the diagnosis and evaluation of the impairments associated with hemiparesis (4546485053).

As it stands, these objective studies of hand function post stroke have focused primarily upon either the lifting or the vertical movement components in object handling. To a certain extent, this limitation has been related to technical restrictions. Other than a handful of studies by Hermsdorfer et al. (849), research in this field has predominantly used manipulanda designed for the study of precision grip, where strain gauge force transducers are attached to a separate base unit [e.g., (232529333537)]. These devices cannot be freely handled by subjects, much less a person with an upper-limb movement disorder. Indeed, patients with hemiparesis often experience specific impairments with precision grip (53) and regularly use alternative grasping strategies such as whole hand grasping (151654). Previous researchers have hypothesized that these alternative grasp strategies may impact grip force scaling (55) and compromise patient ability to manage hand-object-environment relationships during object manipulation (56).

In a recent study with healthy adult subjects, (57) we demonstrated how an instrumented object with multiple load cells and an integrated inertial measurement unit (58) may be used to examine relationships between different grasp configurations, grip force regulation and object orientation. The purpose of the present investigation was to extend this work to the study of patients with hemiparesis post stroke. The first objective was to compare how four alternative grasp configurations commonly used in daily tasks affect grip force regulation in this population. The second objective was to explore the timing and coordination of the whole task sequence (grasping, lifting, holding, placement and object release). The third and final objective was to evaluate the stability of the hand-held object’s orientation across the different phases of the task.[…]

 

Continue —> Frontiers | Effects of Hand Configuration on the Grasping, Holding, and Placement of an Instrumented Object in Patients With Hemiparesis | Neurology

Figure 1. Illustration of the iBox device and the experimental setup. (A) The iBox instrumented object. (B)Setup for the experimental procedure. Initial positions of the iBox and hand start area are indicated by the dotted lines. The gray shaded rectangle indicates the deposit area for the top grasp task.

 

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[Abstract + References] Synergy-Based FES for Post-Stroke Rehabilitation of Upper-Limb Motor Functions

Abstract

Functional electrical stimulation (FES) is capable of activating muscles that are under-recruited in neurological diseases, such as stroke. Therefore, FES provides a promising technology for assisting upper-limb motor functions in rehabilitation following stroke. However, the full benefits of FES may be limited due to lack of a systematic approach to formulate the pattern of stimulation. Our preliminary work demonstrated that it is feasible to use muscle synergy to guide the generation of FES patterns.In this paper, we present a methodology of formulating FES patterns based on muscle synergies of a normal subject using a programmable multi-channel FES device. The effectiveness of the synergy-based FES was tested in two sets of experiments. In experiment one, the instantaneous effects of FES to improve movement kinematics were tested in three patients post ischemic stroke. Patients performed frontal reaching and lateral reaching tasks, which involved coordinated movements in the elbow and shoulder joints. The FES pattern was adjusted in amplitude and time profile for each subject in each task. In experiment two, a 5-day session of intervention using synergy-based FES was delivered to another three patients, in which patients performed task-oriented training in the same reaching movements in one-hour-per-day dose. The outcome of the short-term intervention was measured by changes in Fugl–Meyer scores and movement kinematics. Results on instantaneous effects showed that FES assistance was effective to increase the peak hand velocity in both or one of the tasks. In short-term intervention, evaluations prior to and post intervention showed improvements in both Fugl–Meyer scores and movement kinematics. The muscle synergy of patients also tended to evolve towards that of the normal subject. These results provide promising evidence of benefits using synergy-based FES for upper-limb rehabilitation following stroke. This is the first step towards a clinical protocol of applying FES as therapeutic intervention in stroke rehabilitation.

I. Introduction

Muscle activation during movement is commonly disrupted due to neural injuries from stroke. A major challenge for stroke rehabilitation is to re-establish the normal ways of muscle activation through a general restoration of motor control, otherwise impairments may be compensated by the motor system through a substitution strategy of task control [1]. In post-stroke intervention, new technologies such as neuromuscular electrical stimulation (NMES) or functional electrical stimulation (FES) offer advantages for non-invasively targeting specific groups of muscles [2]–[4] to restore the pattern of muscle activation. Nevertheless, their effectiveness is limited by lack of a systematic methodology to optimize the stimulation pattern, to implement the optimal strategy in clinical settings, and to design a protocol of training towards the goal of restoring motor functions. This pioneer study addresses these issues in clinical application with a non-invasive FES technology.

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1. M. F. Levin, J. A. Kleim, and S. L. Wolf, “What do motor ‘recovery’ and ‘compensation’ mean in patients following stroke?” Neurorehabilitation Neural Repair, vol. 23, no. 4, pp. 313–319, 2008.

2. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation (FES) may modify the poor prognosis of stroke survivors with severe motor loss of the upper extremity: A preliminary study,” Amer. J. Phys. Med. Rehabil., vol. 87, no. 8, pp. 627–636, 2008.

3. W. Rong, “A neuromuscular electrical stimulation (NMES) and robot hybrid system for multi-joint coordinated upper limb rehabilitation after stroke,” J. Neuroeng. Rehabil., vol. 14, no. 1, p. 34, Dec. 2017.

4. J. J. Daly, “Recovery of coordinated gait: Randomized controlled stroke trial of functional electrical stimulation (FES) versus no FES, with weight-supported treadmill and over-ground training,” Neurorehabilitation Neural Repair, vol. 25, no. 7, pp. 588–596, Sep. 2011.

5. R. Nataraj, M. L. Audu, R. F. Kirsch, and R. J. Triolo, “Comprehensive joint feedback control for standing by functional neuromuscular stimulation—A simulation study,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 18, no. 6, pp. 646–657, Dec. 2010.

6. R. Nataraj, M. L. Audu, and R. J. Triolo, “Restoring standing capabilities with feedback control of functional neuromuscular stimulation following spinal cord injury,” Med. Eng. Phys., vol. 42, pp. 13–25, Apr. 2017.

7. H. Rouhani, M. Same, K. Masani, Y. Q. Li, and M. R. Popovic, “PID controller design for FES applied to ankle muscles in neuroprosthesis for standing balance,” Frontiers Neurosci., vol. 11, p. 347, Jun. 2017.

8. V. K. Mushahwar, P. L. Jacobs, R. A. Normann, R. J. Triolo, and N. Kleitman, “New functional electrical stimulation approaches to standing and walking,” J. Neural Eng., vol. 4, no. 3, pp. S181–S197, Sep. 2007.

9. B. J. Holinski, “Intraspinal microstimulation produces over-ground walking in anesthetized cats,” J. Neural Eng., vol. 13, no. 5, p. 056016, Oct. 2016.

10. M. B. Popovic, D. B. Popovic, T. Sinkjær, A. Stefanovic, and L. Schwirtlich, “Restitution of reaching and grasping promoted by functional electrical therapy,” Artif. Organs, vol. 26, no. 3, pp. 271–275, Mar. 2002.

11. A. B. Ajiboye, “Restoration of reaching and grasping movements through brain-controlled muscle stimulation in a person with tetraplegia: A proof-of-concept demonstration,” Lancet Lond. Engl., vol. 389, no. 10081, pp. 1821–1830, May 2017.

12. J. H. Grill and P. H. Peckham, “Functional neuromuscular stimulation for combined control of elbow extension and hand grasp in C5 and C6 quadriplegics,” IEEE Trans. Rehabil. Eng., vol. 6, no. 2, pp. 190–199, Jun. 1998.

13. M. R. Popovic, T. A. Thrasher, M. E. Adams, V. Takes, V. Zivanovic, and M. I. Tonack, “Functional electrical therapy: Retraining grasping in spinal cord injury,” Spinal Cord, vol. 44, no. 3, pp. 143–151, Mar. 2006.

14. C. Ethier, E. R. Oby, M. J. Bauman, and L. E. Miller, “Restoration of grasp following paralysis through brain-controlled stimulation of muscles,” Nature, vol. 485, no. 7398, pp. 368–371, May 2012.

15. G. Alon, “Use of neuromuscular electrical stimulation in neureorehabilitation: A challenge to all,” J. Rehabil. Res. Develop., vol. 40, no. 6, pp. 9–12, Dec. 2003.

16. G. Alon, A. F. Levitt, and P. A. McCarthy, “Functional electrical stimulation enhancement of upper extremity functional recovery during stroke rehabilitation: A pilot study,” Neurorehabilitation Neural Repair, vol. 21, no. 3, pp. 207–215, Jun. 2007.

17. C. Church, C. Price, A. D. Pandyan, S. Huntley, R. Curless, and H. Rodgers, “Randomized controlled trial to evaluate the effect of surface neuromuscular electrical stimulation to the shoulder after acute stroke,” Stroke, vol. 37, no. 12, pp. 2995–3001, Dec. 2006.

18. J. H. Cauraugh and S. B. Kim, “Chronic stroke motor recovery: Duration of active neuromuscular stimulation,” J. Neurolog. Sci., vol. 215, nos. 1–2, pp. 13–19, Nov. 2003.

19. S. Ferrante, T. Schauer, G. Ferrigno, J. Raisch, and F. Molteni, “The effect of using variable frequency trains during functional electrical stimulation cycling,” Neuromodulation, Technol. Neural Interface, vol. 11, no. 3, pp. 216–226, Jul. 2008.

20. R. W. Fields, “Electromyographically triggered electric muscle stimulation for chronic hemiplegia,” Arch. Phys. Med. Rehabil., vol. 68, no. 7, pp. 407–414, Jul. 1987.

21. G. H. Kraft, S. S. Fitts, and M. C. Hammond, “Techniques to improve function of the arm and hand in chronic hemiplegia,” Arch. Phys. Med. Rehabil., vol. 73, no. 3, pp. 220–227, Mar. 1992.

22. G. van Overeem Hansen, “EMG-controlled functional electrical stimulation of the paretic hand,” Scand. J. Rehabil. Med., vol. 11, no. 4, pp. 189–193, 1979.

23. J. H. Cauraugh, S. B. Kim, and A. Duley, “Coupled bilateral movements and active neuromuscular stimulation: Intralimb transfer evidence during bimanual aiming,” Neurosci. Lett., vol. 382, nos. 1–2, pp. 39–44, Jul. 2005.

24. J. S. Knutson, D. D. Gunzler, R. D. Wilson, and J. Chae, “Contralaterally controlled functional electrical stimulation improves hand dexterity in chronic hemiparesis: A randomized trial,” Stroke, vol. 47, no. 10, pp. 2596–2602, Oct. 2016.

25. D. A. E. Bolton, J. H. Cauraugh, and H. A. Hausenblas, “Electromyogram-triggered neuromuscular stimulation and stroke motor recovery of arm/hand functions: A meta-analysis,” J. Neurol. Sci., vol. 223, no. 2, pp. 121–127, Aug. 2004.

26. M. K.-L. Chan, R. K.-Y. Tong, and K. Y.-W. Chung, “Bilateral upper limb training with functional electric stimulation in patients with chronic stroke,” Neurorehabilitation Neural Repair, vol. 23, no. 4, pp. 357–365, May 2009.

27. J. B. Manigandan, G. S. Ganesh, M. Pattnaik, and P. Mohanty, “Effect of electrical stimulation to long head of biceps in reducing gleno humeral subluxation after stroke,” Neuro Rehabil., vol. 34, no. 2, pp. 245–252, 2014.

28. S. Li, C. Zhuang, C. M. Niu, Y. Bao, Q. Xie, and N. Lan, “Evaluation of functional correlation of task-specific muscle synergies with motor performance in patients poststroke,” Frontiers Neurol., vol. 8, p. 337, Jul. 2017.

29. A. d’Avella, P. Saltiel, and E. Bizzi, “Combinations of muscle synergies in the construction of a natural motor behavior,” Nature Neurosci., vol. 6, no. 3, pp. 300–308, Mar. 2003.

30. V. C. K. Cheung, “Muscle synergy patterns as physiological markers of motor cortical damage,” Proc. Nat. Acad. Sci. USA, vol. 109, no. 36, pp. 14652–14656, Sep. 2012.

31. D. J. Clark, L. H. Ting, F. E. Zajac, R. R. Neptune, and S. A. Kautz, “Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke,” J. Neurophysiol., vol. 103, no. 2, pp. 844–857, Feb. 2010.

32. E. Ambrosini, “Neuro-mechanics of recumbent leg cycling in post-acute stroke patients,” Ann. Biomed. Eng., vol. 44, pp. 3238–3251, Jun. 2016.

33. C. Zhuang, J. C. Marquez, H. E. Qu, X. He, and N. Lan, “A neuromuscular electrical stimulation strategy based on muscle synergy for stroke rehabilitation,” in Proc. IEEE 7th Int./EMBS Conf. Neural Eng. (NER), vol. 15, Apr. 2015, pp. 816–819.

34. R. S. Razavian, B. Ghannadi, N. Mehrabi, M. Charlet, and J. McPhee, “Feedback control of functional electrical stimulation for 2-D arm reaching movements,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 26, no. 10, pp. 2033–2043, Oct. 2018.

35. C. M. Niu, C. Zhuang, Y. Bao, S. Li, N. Lan, and Q. Xie, “Synergy-based NMES intervention accelerated rehabilitation of post-stroke hemiparesis,” in Proc. Assoc. Acad. Physiatrists Annu. Conf., Las Vegas, NV, USA, 2017.

36. H. Qu, “Development of network-based multichannel neuromuscular electrical stimulation system for stroke rehabilitation,” J. Rehabil. Res. Develop., vol. 52, no. 3, pp. 263–278, 2016.

37. C. M. Niu, “Effectiveness of short-term training with a synergy-based FES paradigm on motor function recovery post stroke,” in Proc. 12th Int. Soc. Phys. Rehabil. Med. World Congr., Paris, France, 2018.

38. T. Wang, “Customization of synergy-based FES for post-stroke rehabilitation of upper-limb motor functions,” in Proc. IEEE 40th Annu. Int. Conf. Eng. Med. Biol. Soc. (EMBS), Jul. 2018, 3541–3544.

39. L. L. Baker, D. R. McNeal, L. A. Benton, B. R. Bowman, and R. L. Waters, Ed., Neuromuscular Electrical Stimulation a Practical Guide, 4th ed. Downey, CA, USA: Los Amigos Research & Education Institute, 2000.

40. A. d’Avella, A. Portone, L. Fernandez, and F. Lacquaniti, “Control of fast-reaching movements by muscle synergy combinations.,” J. Neurosci., vol. 26, no. 30, pp. 7791–7810, Jul. 2006.

41. R. D. Wilson, “Upper-limb recovery after stroke: A randomized controlled trial comparing EMG-triggered, cyclic, and sensory electrical stimulation,” Neurorehabilitation Neural Repair, vol. 30, no. 10, pp. 978–987, Nov. 2016.

42. A. J. Levine, “Identification of a cellular node for motor control pathways,” Nature Neurosci., vol. 17, no. 4, pp. 586–593, Apr. 2014.

43. S. B. Frost, S. Barbay, K. M. Friel, E. J. Plautz, and R. J. Nudo, “Reorganization of remote cortical regions after ischemic brain injury: A potential substrate for stroke recovery,” J. Neurophysiol., vol. 89, no. 6, pp. 3205–3214, Jun. 2003.

44. P. Langhorne, J. Bernhardt, and G. Kwakkel, “Stroke rehabilitation,” Lancet, vol. 377, no. 9778, pp. 1693–1702, May 2011.

45. M. D. Ellis, B. G. Holubar, A. M. Acosta, R. F. Beer, and J. P. A. Dewald, “Modifiability of abnormal isometric elbow and shoulder joint torque coupling after stroke,” Muscle Nerve, vol. 32, pp. 170–178, Aug. 2005.

46. J. P. A. Dewald, P. S. Pope, J. D. Given, T. S. Buchanan, and W. Z. Rymer, “Abnormal muscle coactivation patterns during isometric torque generation at the elbow and shoulder in hemiparetic subjects,” Brain, vol. 118, no. 2, pp. 495–510, 1995.

47. D. G. Kamper, A. N. McKenna-Cole, L. E. Kahn, and D. J. Reinkensmeyer, “Alterations in reaching after stroke and their relation to movement direction and impairment severity,” Arch. Phys. Med. Rehabil., vol. 83, no. 5, pp. 702–707, May 2002.

48. C. L. Massie, S. Fritz, and M. P. Malcolm, “Elbow extension predicts motor impairment and performance after stroke,” Rehabil. Res. Pract., vol. 2011, pp. 1–7, 2011.

49. V. C. K. Cheung, L. Piron, M. Agostini, S. Silvoni, A. Turolla, and E. Bizzi, “Stability of muscle synergies for voluntary actions after cortical stroke in humans,” Proc. Nat. Acad. Sci. USA, vol. 106, no. 46, pp. 19563–19568, Nov. 2009.

50. J. Roh, W. Z. Rymer, and R. F. Beer, “Robustness of muscle synergies underlying three-dimensional force generation at the hand in healthy humans,” J. Neurophysiol., vol. 107, no. 8, pp. 2123–2142, Apr. 2012.

51. J. Roh, W. Z. Rymer, and R. F. Beer, “Evidence for altered upper extremity muscle synergies in chronic stroke survivors with mild and moderate impairment,” Frontiers Hum. Neurosci., vol. 9, p. 6, Feb. 2015.

52. J. Roh, W. Z. Rymer, E. J. Perreault, S. B. Yoo, and R. F. Beer, “Alterations in upper limb muscle synergy structure in chronic stroke survivors,” J. Neurophysiol., vol. 109, no. 3, pp. 768–781, Feb. 2013.

53. W. H. Backes, W. H. Mess, V. van Kranen-Mastenbroek, and J. P. H. Reulen, “Somatosensory cortex responses to median nerve stimulation: fMRI effects of current amplitude and selective attention,” Clin. Neurophysiol., vol. 111, no. 10, pp. 1738–1744, Oct. 2000.

54. G. Francisco, “Electromyogram-triggered neuromuscular stimulation for improving the arm function of acute stroke survivors: A randomized pilot study,” Arch. Phys. Med. Rehabil., vol. 79, no. 5, pp. 570–575, May 1998.

55. S. K. Sabut, C. Sikdar, R. Kumar, and M. Mahadevappa, “Functional electrical stimulation of dorsiflexor muscle: Effects on dorsiflexor strength, plantarflexor spasticity, and motor recovery in stroke patients,” Neurorehabilitation, vol. 29, no. 4, pp. 393–400, 2011.

56. Y.-H. Wang, F. Meng, Y. Zhang, M.-Y. Xu, and S.-W. Yue, “Full-movement neuromuscular electrical stimulation improves plantar flexor spasticity and ankle active dorsiflexion in stroke patients: A randomized controlled study,” Clin. Rehabil., vol. 30, no. 6, pp. 577–586, Jun. 2016.

57. W. H. Chang and Y.-H. Kim, “Robot-assisted therapy in stroke rehabilitation,” J. Stroke, vol. 15, no. 3, p. 174, 2013.

58. H. G. Wu, Y. R. Miyamoto, L. N. G. Castro, B. P. Ölveczky, and M. A. Smith, “Temporal structure of motor variability is dynamically regulated and predicts motor learning ability,” Nature Neurosci., vol. 17, no. 2, pp. 312–321, Jan. 2014.

59. J. Frère and F. Hug, “Between-subject variability of muscle synergies during a complex motor skill,” Frontiers Comput. Neurosci., vol. 6, p. 99, Dec. 2012.

60. S. Muceli, A. T. Boye, A. d’Avella, and D. Farina, “Identifying representative synergy matrices for describing muscular activation patterns during multidirectional reaching in the horizontal plane,” J. Neurophysiol., vol. 103, no. 3, pp. 1532–1542, Mar. 2010.

61. J. F. Soechting and F. Lacquaniti, “Invariant characteristics of a pointing movement in man,” J. Neurosci., vol. 1, no. 7, pp. 710–720, Jul. 1981.

62. B. Cesqui, A. d’Avella, A. Portone, and F. Lacquaniti, “Catching a ball at the right time and place: Individual factors matter,” PLoS ONE, vol. 7, no. 2, p. e31770, Feb. 2012.

 

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