Abstract W P135: Usability of a Virtual Reality Tool for In-Home Stroke Rehabilitation: A Case Series
Bibliographic record
Abstract
Stroke is the leading cause of serious, long-term disability in the United States. Exercise and activity in the chronic phase of stroke have shown to be beneficial in achieving and maintaining improvements in motor and ADL function. Interactive video games and virtual reality (VR) have also shown to be effective in improving motor function following stroke. We developed a prototype VR rehabilitation tool called Mystic Isle that uses the Microsoft Kinect sensor and a standard PC. The purpose of this case series was to explore the usability of Mystic Isle as a 5-week intervention to improve performance of client identified activities for adults (>21 years) with chronic stroke (>6 months post) in the home setting. Three participants with chronic stroke completed the five week intervention. Outcomes were measured at baseline, immediately pre-intervention (after a 2 week delay from baseline), and immediately post-intervention. The intervention was conducted in each participant’s home. The results of the Canadian Occupational Performance Measure at baseline guided the choice of the virtual tasks for the intervention. The participants were instructed to complete 4 hours each week. The primary outcome was usability and feasibility of the intervention measured with the Game Experience Questionnaire, the System Usability Questionnaire, and a semi-structured interview. Primary outcome data were analyzed using a mixed methods grounded theory approach. Secondary outcomes included motor impairment, quality of life and occupational performance measured using standard clinical measures. Two of the three participants had very positive responses to the intervention and use of the VR tool in the home setting. All three participants demonstrated improvements in quality of life from pre- to post-intervention and improvements in motor function were scattered across participants. All participants expressed the desire to use the system in the future if improvements were made to increase the usability of the system for the home setting. They also spoke about how they attempted to integrate the game into their daily routine or schedule. A major barrier to use for all participants was a busy day that left them too fatigued to interact with the system and complete the intervention.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".