Dataset Information

Myoelectric Computer Interface Training for Reducing Co-Activation and Enhancing Arm Movement in Chronic Stroke Survivors: A Randomized Trial.

ABSTRACT: BACKGROUND:Abnormal muscle co-activation contributes to impairment after stroke. We developed a myoelectric computer interface (MyoCI) training paradigm to reduce abnormal co-activation. MyoCI provides intuitive feedback about muscle activation patterns, enabling decoupling of these muscles. OBJECTIVE:To investigate tolerability and effects of MyoCI training of 3 muscle pairs on arm motor recovery after stroke, including effects of training dose and isometric versus movement-based training. METHODS:We randomized chronic stroke survivors with moderate-to-severe arm impairment to 3 groups. Two groups tested different doses of isometric MyoCI (60 vs 90 minutes), and one group tested MyoCI without arm restraint (90 minutes), over 6 weeks. Primary outcome was arm impairment (Fugl-Meyer Assessment). Secondary outcomes included function, spasticity, and elbow range-of-motion at weeks 6 and 10. RESULTS:Over all 32 subjects, MyoCI training of 3 muscle pairs significantly reduced impairment (Fugl-Meyer Assessment) by 3.3 ± 0.6 and 3.1 ± 0.7 ( P < 10-4) at weeks 6 and 10, respectively. Each group improved significantly from baseline; no significant differences were seen between groups. Participants' lab-based and home-based function also improved at weeks 6 and 10 ( P ? .01). Spasticity also decreased over all subjects, and elbow range-of-motion improved. Both moderately and severely impaired patients showed significant improvement. No participants had training-related adverse events. MyoCI reduced abnormal co-activation, which appeared to transfer to reaching in the movement group. CONCLUSIONS:MyoCI is a well-tolerated, novel rehabilitation tool that enables stroke survivors to reduce abnormal co-activation. It may reduce impairment and spasticity and improve arm function, even in severely impaired patients.

SUBMITTER: Mugler EM

PROVIDER: S-EPMC6611547 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature

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Publications

Myoelectric Computer Interface Training for Reducing Co-Activation and Enhancing Arm Movement in Chronic Stroke Survivors: A Randomized Trial.

Mugler Emily M EM Tomic Goran G Singh Aparna A Hameed Saad S Lindberg Eric W EW Gaide Jon J Alqadi Murad M Robinson Elizabeth E Dalzotto Katherine K Limoli Camila C Jacobson Tyler T Lee Jungwha J Slutzky Marc W MW

Neurorehabilitation and neural repair 20190319 4

<h4>Background</h4>Abnormal muscle co-activation contributes to impairment after stroke. We developed a myoelectric computer interface (MyoCI) training paradigm to reduce abnormal co-activation. MyoCI provides intuitive feedback about muscle activation patterns, enabling decoupling of these muscles.<h4>Objective</h4>To investigate tolerability and effects of MyoCI training of 3 muscle pairs on arm motor recovery after stroke, including effects of training dose and isometric versus movement-based ...[more]

PMID: 30888251

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Project description:The wide variation in upper extremity motor impairments among stroke survivors necessitates more intelligent methods of customized therapy. However, current strategies for characterizing individual motor impairments are limited by the use of traditional clinical assessments (e.g., Fugl-Meyer) and simple engineering metrics (e.g., goal-directed performance). Our overall approach is to statistically identify the range of volitional movement capabilities, and then apply a robot-applied force vector field intervention that encourages under-expressed movements. We investigated whether explorative training with such customized force fields would improve stroke survivors' (n = 11) movement patterns in comparison to a control group that trained without forces (n = 11). Force and control groups increased Fugl-Meyer UE scores (average of 1.0 and 1.1, respectively), which is not considered clinically meaningful. Interestingly, participants from both groups demonstrated dramatic increases in their range of velocity during exploration following only six days of training (average increase of 166.4% and 153.7% for the Force and Control group, respectively). While both groups showed evidence of improvement, we also found evidence that customized forces affected learning in a systematic way. When customized forces were active, we observed broader distributions of velocity that were not present in the controls. Second, we found that these changes led to specific changes in unassisted motion. In addition, while the shape of movement distributions changed significantly for both groups, detailed analysis of the velocity distributions revealed that customized forces promoted a greater proportion of favorable changes. Taken together, these results provide encouraging evidence that patient-specific force fields based on individuals' movement statistics can be used to create new movement patterns and shape them in a customized manner. To the best of our knowledge, this paper is the first to directly link engineering assessments of stroke survivors' exploration movement behaviors to the design of customized robot therapy.

Project description:BackgroundThe key for successful stroke upper-limb rehabilitation includes the personalization of therapeutic interventions based on patients' functional ability and performance level. However, therapists often encounter challenges in supporting personalized rehabilitation due to the lack of information about how stroke survivors use their stroke-affected arm outside the clinic. Wearable technologies have been considered as an effective, objective solution to monitor patients' arm use patterns in their naturalistic environments. However, these technologies have remained a proof of concept and have not been adopted as mainstream therapeutic products, and we lack understanding of how key stakeholders perceive the use of wearable technologies in their practice.ObjectiveWe aim to understand how stroke survivors and therapists perceive and envision the use of wearable sensors and arm activity data in practical settings and how we could design a wearable-based performance monitoring system to better support the needs of the stakeholders.MethodsWe conducted semi-structured interviews with four stroke survivors and 15 occupational therapists (OTs) based on real-world arm use data that we collected for contextualization. To situate our participants, we leveraged a pair of finger-worn accelerometers to collect stroke survivors' arm use data in real-world settings, which we used to create study probes for stroke survivors and OTs, respectively. The interview data was analyzed using the thematic approach.ResultsOur study unveiled a detailed account of (1) the receptiveness of stroke survivors and OTs for using wearable sensors in clinical practice, (2) OTs' envisioned strategies to utilize patient-generated sensor data in the light of providing patients with personalized therapy programs, and (3) practical challenges and design considerations to address for the accelerated integration of wearable systems into their practice.ConclusionsThese findings offer promising directions for the design of a wearable solution that supports OTs to develop individually-tailored therapy programs for stroke survivors to improve their affected arm use.

Project description:BackgroundAbnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However, it is unclear to what extent the paradigm induced changes in the overall intermuscular coordination in the arm, as opposed to changing just the muscles trained with the MyoCI. This study examined the intermuscular coordination patterns of thirty-two stroke survivors who participated in 6 weeks of MyoCI training.MethodsWe used non-negative matrix factorization to identify the arm muscle synergies (coordinated patterns of muscle activity) during a reaching task before and after the training. We examined the extent to which synergies changed as the training reduced motor impairment. In addition, we introduced a new synergy analysis metric, disparity index (DI), to capture the changes in the individual muscle weights within a synergy.ResultsThere was no consistent pattern of change in the number of synergies across the subjects after the training. The composition of muscle synergies, calculated using a traditional synergy similarity metric, also did not change after the training. However, the disparity of muscle weights within synergies increased after the training in the participants who responded to MyoCI training-that is, the specific muscles that the MyoCI was targeting became less correlated within a synergy. This trend was not observed in participants who did not respond to the training.ConclusionsThese findings suggest that MyoCI training reduced arm impairment by decoupling only the muscles trained while leaving other muscles relatively unaffected. This suggests that, even after injury, the nervous system is capable of motor learning on a highly fractionated level. It also suggests that MyoCI training can do what it was designed to do-enable stroke survivors to reduce abnormal co-activation in targeted muscles. Trial registration This study was registered at ClinicalTrials.gov (NCT03579992, Registered 09 July 2018-Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03579992?term=NCT03579992&draw=2&rank=1 ).

Project description:To date, the limited degrees of freedom (DOF) of most robotic training devices hinders them from providing functional training following stroke. We developed a 6-DOF exoskeleton ("BONES") that allows movement of the upper limb to assist in rehabilitation. The objectives of this pilot study were to evaluate the impact of training with BONES on function of the affected upper limb, and to assess whether multijoint functional robotic training would translate into greater gains in arm function than single joint robotic training also conducted with BONES.Twenty subjects with mild to moderate chronic stroke participated in this crossover study. Each subject experienced multijoint functional training and single joint training three sessions per week, for four weeks, with the order of presentation randomized. The primary outcome measure was the change in Box and Block Test (BBT). The secondary outcome measures were the changes in Fugl-Meyer Arm Motor Scale (FMA), Wolf Motor Function Test (WMFT), Motor Activity Log (MAL), and quantitative measures of strength and speed of reaching. These measures were assessed at baseline, after each training period, and at a 3-month follow-up evaluation session.Training with the robotic exoskeleton resulted in significant improvements in the BBT, FMA, WMFT, MAL, shoulder and elbow strength, and reaching speed (p < 0.05); these improvements were sustained at the 3 month follow-up. When comparing the effect of type of training on the gains obtained, no significant difference was noted between multijoint functional and single joint robotic training programs. However, for the BBT, WMFT and MAL, inequality of carryover effects were noted; subsequent analysis on the change in score between the baseline and first period of training again revealed no difference in the gains obtained between the types of training.Training with the 6 DOF arm exoskeleton improved motor function after chronic stroke, challenging the idea that robotic therapy is only useful for impairment reduction. The pilot results presented here also suggest that multijoint functional robotic training is not decisively superior to single joint robotic training. This challenges the idea that functionally-oriented games during training is a key element for improving behavioral outcomes.NCT01050231.

Dataset Information

Myoelectric Computer Interface Training for Reducing Co-Activation and Enhancing Arm Movement in Chronic Stroke Survivors: A Randomized Trial.

Publications

Myoelectric Computer Interface Training for Reducing Co-Activation and Enhancing Arm Movement in Chronic Stroke Survivors: A Randomized Trial.

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