Head-Mounted Display-Based Virtual Reality and Physiological Computing for Stroke Rehabilitation: A Systematic Review
Bibliographic record
Abstract
Virtual reality (VR)-mediated rehabilitation is emerging as a useful tool for stroke survivors to recover motor function. Recent studies are showing that VR coupled with physiological computing (i.e., real-time measurement and analysis of different behavioral and psychophysiological signals) and feedback can lead to 1) more engaged and motivated patients, 2) reproducible treatments that can be performed at the comfort of the patient’s home, and 3) development of new proxies of intervention outcomes and success. While such systems have shown great potential for stroke rehabilitation, an extensive review of the literature is still lacking. Here, we aim to fill this gap and conduct a systematic review of the twelve studies that passed the inclusion criteria. A detailed analysis of the papers was conducted along with a quality assessment/risk of bias evaluation of each study. It was found that the quality of the majority of the studies ranked as either good or fair. Study outcomes also showed that VR-based rehabilitation protocols coupled with physiological computing can enhance patient adherence, improve motivation, overall experience, and ultimately, rehabilitation effectiveness and faster recovery times. Limitations of the examined studies are discussed, such as small sample sizes and unbalanced male/female participant ratios, which could limit the generalizability of the obtained findings. Finally, some recommendations for future studies are given.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".