Feasibility of school-based computer-assisted robotic gaming technology for upper limb rehabilitation of children with cerebral palsy
Bibliographic record
Abstract
INTRODUCTION: We investigated the feasibility of using computer-assisted arm rehabilitation (CAAR) computer games in schools. Outcomes were children's preference for single player or dual player mode, and changes in arm activity and kinematics. METHOD: Nine boys and two girls with cerebral palsy (6-12 years, mean 9 years) played assistive technology computer games in single-user mode or with school friends in an AB-BA design. Preference was determined by recording the time spent playing each mode and by qualitative feedback. We used the ABILHAND-kids and Canadian Occupational Performance Measure to evaluate activity limitation, and a portable laptop-based device to capture arm kinematics. RESULTS: No difference was recorded between single-user and dual-user modes (median daily use 9.27 versus 11.2 min, p = 0.214). Children reported dual-user mode was preferable. There were no changes in activity limitation (ABILHAND-kids, p = 0.424; COPM, p = 0.484) but we found significant improvements in hand speed (p = 0.028), smoothness (p = 0.005) and accuracy (p = 0.007). CONCLUSION: School timetables prohibit extensive use of rehabilitation technology but there is potential for its short-term use to supplement a rehabilitation program. The restricted access to the rehabilitation games was sufficient to improve arm kinematics but not arm activity. Implications for Rehabilitation School premises and teaching staff present no obstacles to the installation of rehabilitation gaming technology. Twelve minutes per day is the average amount of time that the school time table permits children to use rehabilitation gaming equipment (without disruption to academic attendance). The use of rehabilitation gaming technology for an average of 12 minutes daily does not appear to benefit children's functional performance, but there are improvements in the kinematics of children's upper limb.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".