↓ Skip to main content

EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, January 2013
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

twitter
1 tweeter
patent
1 patent

Citations

dimensions_citation
108 Dimensions

Readers on

mendeley
271 Mendeley
citeulike
1 CiteULike
Title
EMG-based pattern recognition approach in post stroke robot-aided rehabilitation: a feasibility study
Published in
Journal of NeuroEngineering and Rehabilitation, January 2013
DOI 10.1186/1743-0003-10-75
Pubmed ID
Authors

Benedetta Cesqui, Peppino Tropea, Silvestro Micera, Hermano Krebs

Abstract

Several studies investigating the use of electromyographic (EMG) signals in robot-based stroke neuro-rehabilitation to enhance functional recovery. Here we explored whether a classical EMG-based patterns recognition approach could be employed to predict patients' intentions while attempting to generate goal-directed movements in the horizontal plane.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 271 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 <1%
Malaysia 1 <1%
Netherlands 1 <1%
South Africa 1 <1%
Germany 1 <1%
India 1 <1%
Canada 1 <1%
Korea, Republic of 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 261 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 60 22%
Student > Master 54 20%
Researcher 36 13%
Student > Bachelor 20 7%
Student > Doctoral Student 19 7%
Other 37 14%
Unknown 45 17%
Readers by discipline Count As %
Engineering 135 50%
Neuroscience 20 7%
Medicine and Dentistry 20 7%
Computer Science 9 3%
Nursing and Health Professions 9 3%
Other 25 9%
Unknown 53 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 05 September 2019.
All research outputs
#7,012,585
of 22,223,421 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#457
of 1,262 outputs
Outputs of similar age
#56,062
of 175,583 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#1
of 17 outputs
Altmetric has tracked 22,223,421 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,262 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.9. This one has gotten more attention than average, scoring higher than 62% 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 175,583 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.
We're also able to compare this research output to 17 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 94% of its contemporaries.