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Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, September 2020
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

Mentioned by

twitter
6 X users

Citations

dimensions_citation
67 Dimensions

Readers on

mendeley
131 Mendeley
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Title
Classification of Parkinson’s disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach
Published in
Journal of NeuroEngineering and Rehabilitation, September 2020
DOI 10.1186/s12984-020-00756-5
Pubmed ID
Authors

Sanghee Moon, Hyun-Je Song, Vibhash D. Sharma, Kelly E. Lyons, Rajesh Pahwa, Abiodun E. Akinwuntan, Hannes Devos

X Demographics

X Demographics

The data shown below were collected from the profiles of 6 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 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 131 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 16 12%
Researcher 16 12%
Student > Bachelor 13 10%
Student > Master 11 8%
Other 6 5%
Other 19 15%
Unknown 50 38%
Readers by discipline Count As %
Computer Science 19 15%
Engineering 18 14%
Medicine and Dentistry 11 8%
Neuroscience 10 8%
Nursing and Health Professions 3 2%
Other 16 12%
Unknown 54 41%
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 24 October 2020.
All research outputs
#7,373,346
of 23,237,082 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#471
of 1,299 outputs
Outputs of similar age
#157,554
of 400,973 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#15
of 35 outputs
Altmetric has tracked 23,237,082 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 1,299 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.0. 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 400,973 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 60% of its contemporaries.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.