Haptic Feedback-Based Virtual Reality Intervention for a Child With Infantile Hemiplegia: A Case Report
Bibliographic record
Abstract
Virtual reality (VR) refers to an advanced technology that provides real life-like experience in a virtual environment. Numerous commercially available systems provide gaming opportunities with VR, while a few also provide haptic feedback along with VR. In the recent past, VR has been explored as a viable intervention in the field of neurorehabilitation. Although there are promising results for adults with hemiplegia, the research involving children with infantile hemiplegia is in the nascent stage. Infantile hemiplegia is manifested by sensory and motor deficits predominantly on one side of the body resulting in adverse effects on the functionality of the affected side since early life. VR gaming has an intense, motivational component that encourages children to put sustained voluntary effort to use both upper extremities. A six-year-old male with infantile hemiplegia presented with difficulty in using the left upper extremity. Pre-intervention scores of the nine-hole peg test (9HPT) and box and block test (BBT) were used to evaluate the manual dexterity, while those of ABILHAND-kids and functional independence measure for children (WeeFIM self-care section) assessed the functional independence. The child underwent treatment for six weeks (five days/week), with each session lasting for 60 minutes/day that included VR gaming with haptic feedback for 30 minutes and conventional physiotherapy for 30 minutes. Post-intervention scores were recorded and were compared with pre-intervention scores. Marked improvement in left upper extremity function was noted not only objectively by the outcome measures but also subjectively by the parents as well as by the child. Moreover, the child remarked that he enjoyed the therapy sessions. The findings of this report would facilitate the design of further research in this area in the form of larger trials.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".