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Robot-assisted assessment of muscle strength

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, October 2017
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Title
Robot-assisted assessment of muscle strength
Published in
Journal of NeuroEngineering and Rehabilitation, October 2017
DOI 10.1186/s12984-017-0314-2
Pubmed ID
Authors

Marco Toigo, Martin Flück, Robert Riener, Verena Klamroth-Marganska

Abstract

Impairment of neuromuscular function in neurological disorders leads to reductions in muscle force, which may lower quality of life. Rehabilitation robots that are equipped with sensors are able to quantify the extent of muscle force impairment and to monitor a patient during the process of neurorehabilitation with sensitive and objective assessment methods. In this article, we provide an overview of fundamental aspects of muscle function and how the corresponding variables can be quantified by means of meaningful robotic assessments that are primarily oriented towards upper limb neurorehabilitation. We discuss new concepts for the assessment of muscle function, and present an overview of the currently available systems for upper limb measurements. These considerations culminate in practical recommendations and caveats for the rational quantification of force magnitude, force direction, moment of a force, impulse, critical force (neuromuscular fatigue threshold) and state and trait levels of fatigue.

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The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 93 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 93 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 13%
Researcher 10 11%
Student > Master 9 10%
Student > Bachelor 8 9%
Student > Doctoral Student 5 5%
Other 17 18%
Unknown 32 34%
Readers by discipline Count As %
Engineering 14 15%
Nursing and Health Professions 13 14%
Sports and Recreations 9 10%
Unspecified 4 4%
Medicine and Dentistry 3 3%
Other 14 15%
Unknown 36 39%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 November 2017.
All research outputs
#17,919,786
of 23,007,887 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#947
of 1,290 outputs
Outputs of similar age
#232,330
of 324,708 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#27
of 30 outputs
Altmetric has tracked 23,007,887 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,290 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 324,708 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.