Real-Time Avatar-Based Feedback to Enhance the Symmetry of Spatiotemporal Parameters After Stroke: Instantaneous Effects of Different Avatar Views
Bibliographic record
Abstract
Gait asymmetry, one of the hallmarks of post stroke locomotion, often persists despite gait rehabilitation interventions, impacting negatively on functional mobility. Real-time feedback and biological cues have been studied extensively in recent years, but their applicability to post-stroke gait symmetry remain questionable. This proof-of-concept study examined the feasibility and instantaneous effects of real-time visual feedback provided in the form of an avatar in twelve participants with stroke on gait symmetry and other gait-related outcomes. The visual avatar was presented via three different views from the back, front and paretic side. Avatar feedback from the paretic side view showed significant increase in bilateral step length, paretic swing time and treadmill walking speed, but no significant differences were found in symmetry measures in any of the three views. Those who had changes in symmetry ratio >0 were grouped as responders to spatial symmetry improvement in the side view. The responders had a significantly higher Chedoke-McMaster Stroke Assessment foot score and presented with a larger initial step length on the paretic side. Furthermore, all participants provided positive feedback and no adverse effects were observed during the experiment. Overall, these findings suggest that real-time avatar-based feedback can be used as an intervention to improve post-stroke gait asymmetry.
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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) | 0.001 | 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) | 0.003 | 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".