Visual-Haptic Colocation in Robotic Rehabilitation Exercises Using a 2D Augmented-Reality Display
Bibliographic record
Abstract
Haptics-based Virtual Reality (VR) games have been found to be effective in rehabilitation from disability. Augmented Reality (AR) has gained traction in recent years in various domains including gaming, entertainment, and education. In this paper, we integrate spatial AR into robotic rehabilitation to provide colocation between visual and haptic feedback as a human user participates in a rehabilitative game. A comparison between the effectiveness of VR vs AR (i.e., non-colocation vs colocation of vision) is done. Spatial AR is the colocation of vision through the use of projection. Visual-Haptic colocation is the combination of spatial AR and haptic interaction. We also compare each visualization technique in the absence and presence of haptic feedback and cognitive loading (CL) for the human user. The system was evaluated by having 10 able-bodied participants do all 8 different conditions lasting approximately 3 minutes per condition. The results show that spatial AR (corresponding to colocation of visual frame and hand frame) leads to the best user performance when doing the task regardless of the presence or the absence of haptics. It is also observed that for users undergoing cognitive loading, the combination of spatial AR and haptics produces the best result in terms of task completion time.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".