Evidence map›Paper›PMID 30400120›Full record

ArticleRestorative neurology and neuroscience2018

Self-powered robots to reduce motor slacking during upper-extremity rehabilitation: a proof of concept study.

Edward P Washabaugh, Emma Treadway, R Brent Gillespie, C David Remy, Chandramouli Krishnan

Abstract read
In one paragraph

Article in Restorative neurology and neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
–field-weighted citation impact
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

14 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Systematic review of novel technology-based interventions for ischemic stroke.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2021
    Pooled it
  2. Trial
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  9. Human-in-the-Loop Myoelectric Pattern Recognition Control of an Arm-Support Robot to Improve Reaching in Stroke Survivors.IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society · 2025
    Article
  10. Article
  11. [Research advances on functional training robots in burn rehabilitation].Zhonghua shao shang yu chuang mian xiu fu za zhi · 2022
    Article
  12. [Several problems worthy of attention in non-surgical treatment of scar].Zhonghua shao shang za zhi = Zhonghua shaoshang zazhi = Chinese journal of burns · 2021
    Article
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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.

Edward P WashabaughNeuRRo Lab, Department of Physical Medicine and Rehabilitation, Michigan Medicine, Ann Arbor, MI, USA.
Emma TreadwayDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
R Brent GillespieDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
C David RemyDepartment of Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA.
Chandramouli KrishnanNeuRRo Lab, Department of Physical Medicine and Rehabilitation, Michigan Medicine, Ann Arbor, MI, USA.

Funding

Techniques DevelopmentP2CHD086844 · NICHD · MEDICAL UNIVERSITY OF SOUTH CAROLINA · PI GEORGE, MARK S · 2015 to 2024
$12.7M
NRI: Wearable eMbots to Induce Recovery of FunctionR01EB019834 · NIBIB · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DURFEE, WILLIAM K, GILLESPIE, RICHARD BRENT · 2014 to 2016
$854k
NIBIB NIH HHS R01 EB019834NICHD NIH HHS P2C HD086844NIH HHS R01-EB019834
6 · The paper itself

Abstract

backgroundRobotic rehabilitation is a highly promising approach to recover lost functions after stroke or other neurological disorders. Unfortunately, robotic rehabilitation currently suffers from "motor slacking", a phenomenon in which the human motor system reduces muscle activation levels and movement excursions, ostensibly to minimize metabolic- and movement-related costs. Consequently, the patient remains passive and is not fully engaged during therapy. To overcome this limitation, we envision a new class of body-powered robots and hypothesize that motor slacking could be reduced if individuals must provide the power to move their impaired limbs via their own body (i.e., through the motion of a healthy limb).

objectiveTo test whether a body-powered exoskeleton (i.e. robot) could reduce motor slacking during robotic training.

methodsWe developed a body-powered robot that mechanically coupled the motions of the user's elbow joints. We tested this passive robot in two groups of subjects (stroke and able-bodied) during four exercise conditions in which we controlled whether the robotic device was powered by the subject or by the experimenter, and whether the subject's driven arm was engaged or at rest. Motor slacking was quantified by computing the muscle activation changes of the elbow flexor and extensor muscles using surface electromyography.

resultsSubjects had higher levels of muscle activation in their driven arm during self-powered conditions compared to externally-powered conditions. Most notably, subjects unintentionally activated their driven arm even when explicitly told to relax when the device was self-powered. This behavior was persistent throughout the trial and did not wane after the initiation of the trial.

conclusionsOur findings provide novel evidence indicating that motor slacking can be reduced by self-powered robots; thus demonstrating promise for rehabilitation of impaired subjects using this new class of wearable system. The results also serve as a foundation to develop more sophisticated body-powered robots (e.g., with controllable transmissions) for rehabilitation purposes.

Indexed as

RoboticsStrokeAdolescentAdultAgedAnalysis of VarianceElectromyographyExercise TherapyFemaleHumansMaleMiddle AgedMuscle, SkeletalProof of Concept StudyRange of Motion, ArticularStroke RehabilitationRehabilitation robotics, hemiparesis, active engagement, EMG, metabolic cost, wearable, cable robot

Identifiers

PMID30400120
PMCPMC6817341

What OpenQuestion holds

Textmetadata
LicenceTDM
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.