Evidence map›Paper›PMID 33807691›Full record

ArticleSensors (Basel, Switzerland)2021

Development of a Low-Cost, Modular Muscle-Computer Interface for At-Home Telerehabilitation for Chronic Stroke.

Octavio Marin-Pardo, Coralie Phanord, Miranda Rennie Donnelly, Christopher M Laine, Sook-Lei Liew

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 15% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.

  1. Pooled it
  2. Trial
  3. Article
  4. Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Review
  10. Article
  11. Functional and neuromuscular changes inducedFrontiers in neuroergonomics · 2022
    Article
  12. Technological advances in lower-limb tele-rehabilitation: A review of literature.Journal of rehabilitation and assistive technologies engineering
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Octavio Marin-PardoDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 900089, USA.ORCID 0000-0002-8923-254X
Coralie PhanordChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 900089, USA.
Miranda Rennie DonnellyChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 900089, USA.
Christopher M LaineChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA 900089, USA.
Sook-Lei LiewDepartment of Biomedical Engineering, University of Southern California, Los Angeles, CA 900089, USA.ORCID 0000-0001-5935-4215
University of Southern California · US

Funding

Big Data Neuroimaging to Predict Motor Behavior After StrokeK01HD091283 · NICHD · UNIVERSITY OF SOUTHERN CALIFORNIA · PI LIEW, SOOK-LEI · 2017 to 2020
$802k
American Heart Association 16IRG26960017Army Research Office W911NNF-14-D-0005NIH HHS K01HD091283
6 · The paper itself

Abstract

Stroke is a leading cause of long-term disability in the United States. Recent studies have shown that high doses of repeated task-specific practice can be effective at improving upper-limb function at the chronic stage. Providing at-home telerehabilitation services with therapist supervision may allow higher dose interventions targeted to this population. Additionally, muscle biofeedback to train patients to avoid unwanted simultaneous activation of antagonist muscles (co-contractions) may be incorporated into telerehabilitation technologies to improve motor control. Here, we present the development and feasibility of a low-cost, portable, telerehabilitation biofeedback system called Tele-REINVENT. We describe our modular electromyography acquisition, processing, and feedback algorithms to train differentiated muscle control during at-home therapist-guided sessions. Additionally, we evaluated the performance of low-cost sensors for our training task with two healthy individuals. Finally, we present the results of a case study with a stroke survivor who used the system for 40 sessions over 10 weeks of training. In line with our previous research, our results suggest that using low-cost sensors provides similar results to those using research-grade sensors for low forces during an isometric task. Our preliminary case study data with one patient with stroke also suggest that our system is feasible, safe, and enjoyable to use during 10 weeks of biofeedback training, and that improvements in differentiated muscle activity during volitional movement attempt may be induced during a 10-week period. Our data provide support for using low-cost technology for individuated muscle training to reduce unintended coactivation during supervised and unsupervised home-based telerehabilitation for clinical populations, and suggest this approach is safe and feasible. Future work with larger study populations may expand on the development of meaningful and personalized chronic stroke rehabilitation.

Indexed as

StrokeStroke RehabilitationTelerehabilitationComputersHumansMusclesbiofeedbackelectromyographyhuman-computer interfacestroketelerehabilitation

Identifiers

PMID33807691
PMCPMC7961888
OpenAlexW3134720233

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.