↓ 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, July 2013
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)
  • Good Attention Score compared to outputs of the same age and source (79th percentile)

Mentioned by

twitter
1 X user
patent
2 patents

Citations

dimensions_citation
133 Dimensions

Readers on

mendeley
290 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, July 2013
DOI 10.1186/1743-0003-10-75
Pubmed ID
Authors

Benedetta Cesqui, Peppino Tropea, Silvestro Micera, Hermano Igo 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.

X Demographics

X Demographics

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 290 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 280 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 64 22%
Student > Master 54 19%
Researcher 36 12%
Student > Bachelor 22 8%
Student > Doctoral Student 20 7%
Other 38 13%
Unknown 56 19%
Readers by discipline Count As %
Engineering 139 48%
Neuroscience 21 7%
Medicine and Dentistry 19 7%
Nursing and Health Professions 9 3%
Computer Science 9 3%
Other 29 10%
Unknown 64 22%
Attention Score in Context

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 30 January 2024.
All research outputs
#8,262,107
of 25,374,647 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#524
of 1,413 outputs
Outputs of similar age
#67,629
of 206,791 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#6
of 39 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
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 gotten more attention than average, scoring higher than 61% 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 206,791 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 39 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.