Inducing Visuomotor Adaptation Using Virtual Reality Gaming with a Virtual Shift as a Treatment for Unilateral Spatial Neglect
Bibliographic record
Abstract
Unilateral spatial neglect after stroke is characterized by reduced responses to stimuli on the contralesional side, causing significant impairments in self-care and safety. Conventional visuomotor adaptation (VMA) with prisms that cause a lateral shift of the visual scene can decrease neglect symptoms but is not engaging according to patients. Performing VMA within a virtual reality (VR) environment may be more engaging but has never been tested. To determine if VMA can be elicited in a VR environment, healthy subjects (n=7) underwent VMA that was elicited by either wearing prisms that caused an optical shift, or by application of a virtual shift of the hand cursor within the VR environment. A low cost VR system was developed by coupling the Kinect v2 gaming sensor to online games via the Flexible Action and Articulated Skeleton Toolkit (FAAST) software. The adaptation phase of training consisted of a reaching task in online games or in a custom target pointing program. Following the adaptation phase the optical or virtual shift was removed and participants were assessed during the initial portion of the de-adaptation phase for the presence of an after-effect on their reaching movements, with lateral reaching errors indicating the successful induction of VMA. Results show that practicing reaching in a VR environment with a virtual shift lead to a horizontal after-effect similar to conventional prism adaptation. The results demonstrate that VMA can be elicited in a VR environment and suggest that VR gaming therapy could be used to improve recovery from unilateral spatial neglect.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".