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Adaptive training algorithm for robot-assisted upper-arm rehabilitation, applicable to individualised and therapeutic human-robot interaction

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, September 2013
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2 X users

Citations

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23 Dimensions

Readers on

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144 Mendeley
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1 CiteULike
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Title
Adaptive training algorithm for robot-assisted upper-arm rehabilitation, applicable to individualised and therapeutic human-robot interaction
Published in
Journal of NeuroEngineering and Rehabilitation, September 2013
DOI 10.1186/1743-0003-10-102
Pubmed ID
Authors

Radhika Chemuturi, Farshid Amirabdollahian, Kerstin Dautenhahn

Abstract

Rehabilitation robotics is progressing towards developing robots that can be used as advanced tools to augment the role of a therapist. These robots are capable of not only offering more frequent and more accessible therapies but also providing new insights into treatment effectiveness based on their ability to measure interaction parameters. A requirement for having more advanced therapies is to identify how robots can 'adapt' to each individual's needs at different stages of recovery. Hence, our research focused on developing an adaptive interface for the GENTLE/A rehabilitation system. The interface was based on a lead-lag performance model utilising the interaction between the human and the robot. The goal of the present study was to test the adaptability of the GENTLE/A system to the performance of the user.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 X users 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 144 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 2%
Mexico 2 1%
Slovakia 1 <1%
Switzerland 1 <1%
Unknown 137 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 26 18%
Student > Ph. D. Student 22 15%
Researcher 18 13%
Student > Bachelor 10 7%
Student > Doctoral Student 8 6%
Other 28 19%
Unknown 32 22%
Readers by discipline Count As %
Engineering 23 16%
Psychology 16 11%
Computer Science 16 11%
Medicine and Dentistry 14 10%
Neuroscience 10 7%
Other 32 22%
Unknown 33 23%
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 20 October 2013.
All research outputs
#20,657,128
of 25,374,917 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#1,091
of 1,413 outputs
Outputs of similar age
#163,991
of 217,316 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#5
of 10 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
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 is in the 10th percentile – i.e., 10% 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 217,316 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.