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Record W2566667938 · doi:10.1186/s12938-016-0289-4

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

2016· review· en· W2566667938 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioMedical Engineering OnLine · 2016
Typereview
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsMcGill University
FundersDeutsche Forschungsgemeinschaft
KeywordsVirtual realityRehabilitationPhysical medicine and rehabilitationVisualizationMovement (music)Human–computer interactionComputer scienceMedicinePhysical therapyArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.186
Threshold uncertainty score0.980

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.023
GPT teacher head0.339
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it