Body ownership illusion through virtual reality as modulator variable for limbs rehabilitation after stroke: a systematic review
Bibliographic record
Abstract
Abstract Stroke is the leading cause of motor impairments and generates distortion of body representation. Hence, stroke can modulate the sense of embodiment, namely the feeling of being inside the body (ownership), in the place where the body is located (location), and moving the body according to its own intentions (agency). A growing number of studies have adopted virtual reality (VR) to train motor abilities. However, the impact of the body illusion on the rehabilitation outcome is not fully understood. The present systematic review investigates the modulating role of the body illusion elicited by VR on motor rehabilitation in post-stroke patients after embodying a virtual avatar. The research was led in the main databases—PubMed, Scopus, PsychINFO, and Web of Science—and four studies matched the inclusion criteria (e.g., to have a sample of adult post-stroke patients, to use VR as an instrument for motor rehabilitation, to adopt the paradigm of the body illusion as a modulator for motor rehabilitation, to test the sense of body illusion outcome). Research outcomes demonstrated that two studies adopted the immersive and two the non-immersive embodied VR; three studies focused on the upper limb, and one on lower limb rehabilitation. Two studies compare VR training with traditional therapy, and two are pilot studies with only one experimental group. The studies demonstrated the feasibility of the body illusion as an accelerator for motor rehabilitation compared to the non-embodied condition, and as a positive correlator of the rehabilitation outcome. The finding should be taken with caution due to the limited studies included; however, they are encouraging to justify further research efforts in this area.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".