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Sit-stand and stand-sit transitions in older adults and patients with Parkinson’s disease: event detection based on motion sensors versus force plates

Overview of attention for article published in Journal of NeuroEngineering and Rehabilitation, October 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

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2 X users
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2 patents

Citations

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67 Dimensions

Readers on

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192 Mendeley
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Title
Sit-stand and stand-sit transitions in older adults and patients with Parkinson’s disease: event detection based on motion sensors versus force plates
Published in
Journal of NeuroEngineering and Rehabilitation, October 2012
DOI 10.1186/1743-0003-9-75
Pubmed ID
Authors

Agnes Zijlstra, Martina Mancini, Ulrich Lindemann, Lorenzo Chiari, Wiebren Zijlstra

Abstract

Motion sensors offer the possibility to obtain spatiotemporal measures of mobility-related activities such as sit-stand and stand-sit transitions. However, the application of new sensor-based methods for assessing sit-stand-sit performance requires the detection of crucial events such as seat on/off in the sensor-based data. Therefore, the aim of this study was to evaluate the agreement of detecting sit-stand and stand-sit events based on a novel body-fixed-sensor method with a force-plate based analysis.

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 192 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 <1%
Switzerland 1 <1%
Netherlands 1 <1%
Belgium 1 <1%
Japan 1 <1%
United States 1 <1%
Unknown 186 97%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 20%
Student > Master 37 19%
Student > Bachelor 20 10%
Student > Doctoral Student 16 8%
Researcher 14 7%
Other 34 18%
Unknown 33 17%
Readers by discipline Count As %
Engineering 41 21%
Medicine and Dentistry 27 14%
Nursing and Health Professions 16 8%
Sports and Recreations 14 7%
Neuroscience 13 7%
Other 29 15%
Unknown 52 27%
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 01 June 2021.
All research outputs
#7,356,343
of 25,374,647 outputs
Outputs from Journal of NeuroEngineering and Rehabilitation
#447
of 1,413 outputs
Outputs of similar age
#54,556
of 192,637 outputs
Outputs of similar age from Journal of NeuroEngineering and Rehabilitation
#4
of 15 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 69th 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 67% 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 192,637 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 70% of its contemporaries.
We're also able to compare this research output to 15 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 73% of its contemporaries.