The Effectivness of Virtual Reality based Exercise Therapy for Upper Limb Rehabilitation in Sub Acute Stroke: An Experimental Study
Bibliographic record
Abstract
Introduction: Stroke is the third most common cause of death and second most common cause of disability overall. The most prevalent disability that causes dysfunction following a stroke is weakness or paralysis, which often leads to upper limb impairments, Limiting independence in daily activities. (VR) has emerged as recent treatment approach in stroke rehabilitation. VR therapies give stroke survivors the rare chance to engage in a rich environment while receiving scalable, structured training opportunities reinforced by multimodal feedback to improve neuroplasticity and skill acquisition via repeated practice. Aim: To evaluate synergistic effect of VR based exercise therapy along with conventional physiotherapy in improving upper limb motor functions, cognition and quality of life in individuals with stroke. Materials and Methods: Ethical approval for the study was taken from the Institutional Ethical Committee, Punjabi university, Patiala. This single group, pre-post experimental study involved 8 stroke survivors in sub-acute stage of stroke. All participants received VR- based exercise therapy along with conventional exercise therapy, 5 times per week for 4 weeks. Data were collected at day 0 and at day 20th by using the outcome measure tools like Fugl Meyer Assessment-UA, Montreal Cognitive Assessment scale and Stroke impact scale. The analysis of the data was done by using SPSS Software. Results: Significant improvements was observed in Fugl MayerUL (p<0.05), MoCA (p<0.05), and only Emotion (p<0.05) or IADL (p<0.05) domains of Stroke Impact Scale. Conclusion: VR-based exercise therapy along with conventional physiotherapy shows potential to improve motor and cognitive functions in stroke survivors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".