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Attention Score in Context
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
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Published in |
Journal of NeuroEngineering and Rehabilitation, October 2012
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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
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.
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 2 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 50% |
Unknown | 1 | 50% |
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
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.