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Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, July 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • High Attention Score compared to outputs of the same age and source (90th percentile)

Mentioned by

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2 blogs
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Citations

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286 Dimensions

Readers on

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461 Mendeley
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1 CiteULike
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Title
Training modalities in robot-mediated upper limb rehabilitation in stroke: a framework for classification based on a systematic review
Published in
Journal of NeuroEngineering and Rehabilitation, July 2014
DOI 10.1186/1743-0003-11-111
Pubmed ID
Authors

Angelo Basteris, Sharon M Nijenhuis, Arno HA Stienen, Jaap H Buurke, Gerdienke B Prange, Farshid Amirabdollahian

Abstract

Robot-mediated post-stroke therapy for the upper-extremity dates back to the 1990s. Since then, a number of robotic devices have become commercially available. There is clear evidence that robotic interventions improve upper limb motor scores and strength, but these improvements are often not transferred to performance of activities of daily living. We wish to better understand why. Our systematic review of 74 papers focuses on the targeted stage of recovery, the part of the limb trained, the different modalities used, and the effectiveness of each. The review shows that most of the studies so far focus on training of the proximal arm for chronic stroke patients. About the training modalities, studies typically refer to active, active-assisted and passive interaction. Robot-therapy in active assisted mode was associated with consistent improvements in arm function. More specifically, the use of HRI features stressing active contribution by the patient, such as EMG-modulated forces or a pushing force in combination with spring-damper guidance, may be beneficial.Our work also highlights that current literature frequently lacks information regarding the mechanism about the physical human-robot interaction (HRI). It is often unclear how the different modalities are implemented by different research groups (using different robots and platforms). In order to have a better and more reliable evidence of usefulness for these technologies, it is recommended that the HRI is better described and documented so that work of various teams can be considered in the same group and categories, allowing to infer for more suitable approaches. We propose a framework for categorisation of HRI modalities and features that will allow comparing their therapeutic benefits.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 461 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 <1%
Switzerland 2 <1%
Canada 2 <1%
Australia 1 <1%
Sweden 1 <1%
Hong Kong 1 <1%
India 1 <1%
Germany 1 <1%
Singapore 1 <1%
Other 1 <1%
Unknown 447 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 92 20%
Student > Ph. D. Student 75 16%
Student > Bachelor 50 11%
Researcher 45 10%
Student > Doctoral Student 23 5%
Other 63 14%
Unknown 113 25%
Readers by discipline Count As %
Engineering 158 34%
Medicine and Dentistry 46 10%
Nursing and Health Professions 34 7%
Computer Science 24 5%
Neuroscience 19 4%
Other 56 12%
Unknown 124 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 25 April 2019.
All research outputs
#2,310,762
of 25,374,917 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#97
of 1,413 outputs
Outputs of similar age
#22,593
of 240,570 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#2
of 22 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,413 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done particularly well, scoring higher than 93% of its peers.
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 240,570 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 22 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.