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Review of control strategies for robotic movement training after neurologic injury

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, June 2009
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
2 tweeters
patent
3 patents
wikipedia
1 Wikipedia page

Citations

dimensions_citation
744 Dimensions

Readers on

mendeley
1145 Mendeley
citeulike
3 CiteULike
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Title
Review of control strategies for robotic movement training after neurologic injury
Published in
Journal of NeuroEngineering and Rehabilitation, June 2009
DOI 10.1186/1743-0003-6-20
Pubmed ID
Authors

Laura Marchal-Crespo, David J Reinkensmeyer

Abstract

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 1,145 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 9 <1%
Switzerland 7 <1%
Italy 6 <1%
Germany 4 <1%
United Kingdom 4 <1%
Brazil 3 <1%
Malaysia 3 <1%
Hong Kong 2 <1%
Singapore 2 <1%
Other 17 1%
Unknown 1088 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 277 24%
Student > Master 198 17%
Researcher 132 12%
Student > Bachelor 81 7%
Student > Doctoral Student 61 5%
Other 154 13%
Unknown 242 21%
Readers by discipline Count As %
Engineering 600 52%
Medicine and Dentistry 59 5%
Computer Science 39 3%
Neuroscience 32 3%
Agricultural and Biological Sciences 31 3%
Other 100 9%
Unknown 284 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. 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 27 February 2020.
All research outputs
#1,943,073
of 17,067,437 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#104
of 1,063 outputs
Outputs of similar age
#24,278
of 199,211 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#3
of 18 outputs
Altmetric has tracked 17,067,437 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,063 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.8. This one has done particularly well, scoring higher than 90% 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 199,211 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 87% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.