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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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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.

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The data shown below were collected from the profiles of 3 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 475 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 472 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 68 14%
Student > Master 66 14%
Student > Ph. D. Student 59 12%
Researcher 49 10%
Student > Doctoral Student 26 5%
Other 83 17%
Unknown 124 26%
Readers by discipline Count As %
Medicine and Dentistry 58 12%
Nursing and Health Professions 56 12%
Engineering 45 9%
Computer Science 42 9%
Neuroscience 35 7%
Other 94 20%
Unknown 145 31%
Attention Score in Context

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
#13,185,127
of 22,952,268 outputs
Outputs from BioMedical Engineering OnLine
#326
of 823 outputs
Outputs of similar age
#205,090
of 420,838 outputs
Outputs of similar age from BioMedical Engineering OnLine
#5
of 19 outputs
Altmetric has tracked 22,952,268 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 823 research outputs from this source. They receive a mean Attention Score of 4.6. This one has gotten more attention than average, scoring higher than 60% 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 420,838 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 50% of its contemporaries.
We're also able to compare this research output to 19 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.