A gaming system with haptic feedback to improve upper extremity function: A prospective case series
Bibliographic record
Abstract
BACKGROUND: Video games can be used to motivate repetitive movements in paediatric rehabilitation. Most upper limb videogaming therapies do not however include haptic feedback which can limit their impact. OBJECTIVE: To explore the effectiveness of interactive computer play with haptic feedback for improving arm function in children with cerebral palsy (CP). METHODS: Eleven children with hemiplegic CP attended 12 therapist-guided sessions in which they used a gaming station composed of the Novint Falcon, custom-built handles, physical supports for the child’s arm, games, and an application to manage and calibrate therapeutic settings. Outcome measures included Quality of Upper Extremity Skills Test (QUEST) and Canadian Occupational Performance Measure (COPM). The study protocol is registered on clinicaltrials.gov (NCT04298411). RESULTS: Participants completed a mean of 3858 wrist extensions and 6665 elbow/shoulder movements during the therapist-guided sessions. Clinically important improvements were observed on the dissociated and grasp dimensions on the QUEST and the performance and satisfaction scales of the COPM (all p < 0.05). CONCLUSION: This study suggests that computer play with haptic feedback could be a useful and playful option to help improve the hand/arm capacities of children with CP and warrants further study. The opportunities and challenges of using low-cost, mainstream gaming software and hardware for therapeutic applications are discussed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".