Virtual, augmented and mixed reality for motor neurorehabilitation: a scoping review focused on the role of body representation (Preprint)
Bibliographic record
Abstract
BACKGROUND Extended reality (XR), encompassing virtual reality (VR), augmented reality (AR), and mixed reality (MR), is increasingly used in neurorehabilitation to provide multisensory feedback and promote neural plasticity in sensorimotor networks. OBJECTIVE This scoping review aimed to: (1) examine how XR technologies are applied in motor neurorehabilitation; (2) explore how body representation and somatic embodiment are addressed; and (3) analyse the methodological designs of XR-based interventions. METHODS The protocol was registered in PROSPERO (ID: 481092) and conducted in accordance with PRISMA-ScR guidelines. A structured search of PubMed, Embase, Scopus, and Web of Science identified relevant peer-reviewed articles published up to 2023. Studies were included if they: (a) involved XR-based interventions explicitly targeting neurorehabilitation; and (b) reported data on implementation or user outcomes. Exclusion criteria included non-XR studies and reviews lacking original findings. Key variables extracted included study design, participant characteristics, XR devices and software used, and treatment approaches related to somatic embodiment. Methodological quality was assessed using the Newcastle-Ottawa Scale. RESULTS A total of 26 studies were included, primarily clinical trials involving neurological patients. XR technologies have evolved considerably between 2008 and 2023, with the adoption of cost-effective devices such as Oculus Rift and HTC Vive accelerating research. Interventions often targeted sensorimotor deficits, with many studies showing measurable improvements in motor and cognitive function. Both first-person and third-person perspectives were employed, with task-specific advantages for each. Five studies incorporated EEG to monitor brain responses, while two used non-invasive brain stimulation to augment therapeutic effects. Additional tools included eye trackers and motion sensors. Unity 3D was the most widely used development platform for XR applications. CONCLUSIONS Evidence from the reviewed studies supports the effectiveness of XR interventions in enhancing reinforcement learning and facilitating recovery in neurorehabilitation. Tailored XR approaches, grounded in embodiment principles and patient-specific needs, show promise for improving outcomes in neurological rehabilitation programs. CLINICALTRIAL Prospero ID: 481092
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".