Evidence map›Paper›PMID 30558608›Full record

Trial reportJournal of neuroengineering and rehabilitation2018

Muscle fatigue assessment during robot-mediated movements.

Maddalena Mugnosso, Francesca Marini, Michael Holmes, Pietro Morasso, Jacopo Zenzeri

Open access · goldAbstract readClinical Trial
In one paragraph

Trial report in Journal of neuroengineering and rehabilitation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
2.3field-weighted citation impact, top 11% 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

18 citing papers in PubMed, 42 citations in OpenAlex.

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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 at 2 institutions in 2 countries.

Maddalena MugnossoMotor Learning, Assistive and Rehabilitation Robotics Lab, Robotics, Brain and Cognitive Sciences unit, Istituto Italiano di Tecnologia, Genoa, Italy. maddalena.mugnosso@iit.it.ORCID 0000-0003-4897-6902
Francesca MariniMotor Learning, Assistive and Rehabilitation Robotics Lab, Robotics, Brain and Cognitive Sciences unit, Istituto Italiano di Tecnologia, Genoa, Italy.
Michael HolmesDepartment of Kinesiology, Brock University, St. Catharines, ON, Canada.
Pietro MorassoMotor Learning, Assistive and Rehabilitation Robotics Lab, Robotics, Brain and Cognitive Sciences unit, Istituto Italiano di Tecnologia, Genoa, Italy.
Jacopo ZenzeriMotor Learning, Assistive and Rehabilitation Robotics Lab, Robotics, Brain and Cognitive Sciences unit, Istituto Italiano di Tecnologia, Genoa, Italy.
Italian Institute of Technology · ITBrock University · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSeveral neuromuscular disorders present muscle fatigue as a typical symptom. Therefore, a reliable method of fatigue assessment may be crucial for understanding how specific disease features evolve over time and for developing effective rehabilitation strategies. Unfortunately, despite its importance, a standardized, reliable and objective method for fatigue measurement is lacking in clinical practice and this work investigates a practical solution.

methods40 healthy young adults performed a haptic reaching task, while holding a robotic manipulandum. Subjects were required to perform wrist flexion and extension movements in a resistive visco-elastic force field, as many times as possible, until the measured muscles (mainly flexor and extensor carpi radialis) exhibited signs of fatigue. In order to analyze the behavior and the characteristics of the two muscles, subjects were divided into two groups: in the first group, the resistive force was applied by the robot only during flexion movements, whereas, in the second group, the force was applied only during extension movements. Surface electromyographic signals (sEMG) of both flexor and extensor carpi radialis were acquired. A novel indicator to define the Onset of Fatigue (OF) was proposed and evaluated from the Mean Frequency of the sEMG signal. Furthermore, as measure of the subjects' effort throughout the task, the energy consumption was estimated.

resultsFrom the beginning to the end of the task, as expected, all the subjects showed a decrement in Mean Frequency of the muscle involved in movements resisting the force. For the OF indicator, subjects were consistent in terms of timing of fatigue; moreover, extensor and flexor muscles presented similar OF times. The metabolic analysis showed a very low level of energy consumption and, from the behavioral point of view, the test was well tolerated by the subjects.

conclusionThe robot-aided assessment test proposed in this study, proved to be an easy to administer, fast and reliable method for objectively measuring muscular fatigue in a healthy population. This work developed a framework for an evaluation that can be deployed in a clinical practice with patients presenting neuromuscular disorders. Considering the low metabolic demand, the requested effort would likely be well tolerated by clinical populations.

Indexed as

RoboticsAdultElectromyographyFemaleHumansMaleMuscle FatigueMuscle, SkeletalFatigueNeuromuscular disordersRobot-aided assessmentRobotic rehabilitationsEMGUpper limb

Identifiers

PMID30558608
PMCPMC6296130
OpenAlexW2904481607

What OpenQuestion holds

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LicenceCC BY
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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.