Variations in Movement Patterns during Active Video Game Play in Children with Cerebral Palsy
Bibliographic record
Abstract
Aim: Low-cost active video games (AVG) are of growing interest for use in home-based physical therapy regimes.This study investigates typical upper-limb movement patterns and variations during AVG play in children with cerebral palsy.Methods: Sixteen children (9.5 ± 1.6 years) with hemiplegic or diplegic cerebral palsy (GMFCS Level I) participated in the study.A 7-camera Vicon MX 3D Optical Capture System was used to measure and record their upper limb movements as they played three different AVGs on the Nintendo Wii system.Results: Play style during Wii sports games tended to be either realistic or non-realistic.All players used realistic movements when playing Wii Bowling, while 69% (n=11) and 63% (n=10) played realistically during Wii Tennis and Wii Boxing, respectively.Realistic movements tended to elicit greater use of: (a) the more proximal joints, and (b) the non-dominant/hemiplegic limb (in bilateral games).Play style may be influenced by personal or predisposing factors (e.g.MACS level, gender, experience with AVGs). Conclusion: Movement patterns and styles vary widely between children during AVG play with the NintendoWii.The design of AVG-based therapies should consider these variations and their implications in order to maximize therapeutic benefit.Future studies should focus on measuring the efficacy of AVG-based therapies for home use.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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 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".