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Movement visualisation in virtual reality rehabilitation of the lower limb: a systematic review

Overview of attention for article published in BioMedical Engineering OnLine, December 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • High Attention Score compared to outputs of the same age and source (83rd percentile)

Mentioned by

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3 tweeters

Citations

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34 Dimensions

Readers on

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343 Mendeley
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Title
Movement visualisation in virtual reality rehabilitation of the lower limb: a systematic review
Published in
BioMedical Engineering OnLine, December 2016
DOI 10.1186/s12938-016-0289-4
Pubmed ID
Authors

Luara Ferreira dos Santos, Oliver Christ, Kedar Mate, Henning Schmidt, Jörg Krüger, Christian Dohle

Abstract

Virtual reality (VR) based applications play an increasing role in motor rehabilitation. They provide an interactive and individualized environment in addition to increased motivation during motor tasks as well as facilitating motor learning through multimodal sensory information. Several previous studies have shown positive effect of VR-based treatments for lower extremity motor rehabilitation in neurological conditions, but the characteristics of these VR applications have not been systematically investigated. The visual information on the user's movement in the virtual environment, also called movement visualisation (MV), is a key element of VR-based rehabilitation interventions. The present review proposes categorization of Movement Visualisations of VR-based rehabilitation therapy for neurological conditions and also summarises current research in lower limb application. A systematic search of literature on VR-based intervention for gait and balance rehabilitation in neurological conditions was performed in the databases namely; MEDLINE (Ovid), AMED, EMBASE, CINAHL, and PsycInfo. Studies using non-virtual environments or applications to improve cognitive function, activities of daily living, or psychotherapy were excluded. The VR interventions of the included studies were analysed on their MV. In total 43 publications were selected based on the inclusion criteria. Seven distinct MV groups could be differentiated: indirect MV (N = 13), abstract MV (N = 11), augmented reality MV (N = 9), avatar MV (N = 5), tracking MV (N = 4), combined MV (N = 1), and no MV (N = 2). In two included articles the visualisation conditions included different MV groups within the same study. Additionally, differences in motor performance could not be analysed because of the differences in the study design. Three studies investigated different visualisations within the same MV group and hence limited information can be extracted from one study. The review demonstrates that individuals' movements during VR-based motor training can be displayed in different ways. Future studies are necessary to fundamentally explore the nature of this VR information and its effect on motor outcome.

Twitter Demographics

The data shown below were collected from the profiles of 3 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 343 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
India 1 <1%
Portugal 1 <1%
Germany 1 <1%
Unknown 340 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 60 17%
Student > Master 57 17%
Student > Ph. D. Student 47 14%
Researcher 37 11%
Student > Doctoral Student 24 7%
Other 70 20%
Unknown 48 14%
Readers by discipline Count As %
Nursing and Health Professions 50 15%
Medicine and Dentistry 48 14%
Engineering 40 12%
Computer Science 39 11%
Neuroscience 28 8%
Other 71 21%
Unknown 67 20%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 December 2017.
All research outputs
#6,454,324
of 12,306,918 outputs
Outputs from BioMedical Engineering OnLine
#175
of 545 outputs
Outputs of similar age
#131,373
of 330,972 outputs
Outputs of similar age from BioMedical Engineering OnLine
#2
of 12 outputs
Altmetric has tracked 12,306,918 research outputs across all sources so far. This one is in the 47th percentile – i.e., 47% of other outputs scored the same or lower than it.
So far Altmetric has tracked 545 research outputs from this source. They receive a mean Attention Score of 3.2. 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 330,972 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 59% of its contemporaries.
We're also able to compare this research output to 12 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.