Improving Upper Extremity Motor Skills in Girls with Rett Syndrome Using Virtual Reality
Bibliographic record
Abstract
Introduction: Rett Syndrome is a genetic disorder that limits a girl’s ability to use her upper extremities for daily activities, such as dressing and playing. One possible intervention to improve upper extremity function in this population is virtual reality, which can be used to increase activity demands during therapy sessions. Objectives: To determine the feasibility of using internet-based virtual reality intervention for Rett Syndrome (RTT-IVR), to decrease hand wringing/mouthing and increase hand and arm movements away from the midline by identifying attributes and limitations to the proposed intervention. Materials and Methods: Using FAAST Software and Microsoft Kinect sensor, RTT-IVR was trialed with 6 girls with RTT. Upper extremity movements were used to play free Internet games as means of increasing repetitions and purposeful arm movements. Data regarding attributes and limitations of the RTT-IVR intervention were collected via observation and post-session parent interviews. Results: Interviews and observation revealed successful game play when games were motivating, clearly established cause and effect, and matched level of cognitive ability of the participant. Limitations include technological glitches regarding Kinect sensor sensitivity and identifying appropriate games for each participant’s interests and abilities. Conclusion: Internet based virtual reality interventions for girls with RTT should be highly individualized to increase motivation and success of intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".