Personalized Virtual Reality for Upper Extremity Rehabilitation: Moving from the Clinic to a Home Exercise Program
Bibliographic record
Abstract
Introduction: Traditional rehabilitation does not provide adequate repetitions for maximal motor recovery in the clinic and home exercise programs (HEPs) have low compliance rates. Personalized virtual reality (PVR) is a promising low-cost therapeutic tool for improving compliance by incorporating the client's interests, abilities, and goals into a motivating and engaging intervention using internet games. Objectives: The current study aimed to develop and refine a clinic-to-home PVR intervention, determine its feasibility and usability in an outpatient rehabilitation clinic and as a HEP, and examine its effects on motivation/engagement, compliance, motor repetitions, and functional motor performance. Methods: The PVR system utilizes a Microsoft Kinect sensor to track the participants’ movements, free software to translate movements to keystrokes, and free internet games. The therapist matched participants’ interests to internet games, customized therapeutic movements for game play, and increased the movement thresholds for game activation as participants improved. Two participants who had strokes resulting in upper extremity (UE) hemiplegia were recruited. The participants attended outpatient occupational therapy (OT) services twice weekly. Following training, the participants used the PVR system at home in place of their UE HEP. They continued to receive traditional OT once a week and clinic-PVR once a week for 5-8 weeks. Results: The PVR intervention was successfully implemented in the clinic and the clients’ homes. PVR increased motivation and treatment compliance. The clients exhibited improvements in UE active range of motion, function, symptoms, and occupational performance. Conclusion: Preliminary evidence suggests PVR can improve motivation, compliance, function, and occupational performance. However, larger scale studies and protocol refinement are necessary.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".