Integration of Augmented Reality and Assistive Devices for Post-Stroke Hand Opening Rehabilitation
Bibliographic record
Abstract
Impairment of hand function is prevalent among stroke survivors, motivating the search for effective rehabilitation therapy. Recent studies have suggested that for upper extremity functional recovery, repetitive training with virtual reality is helpful. Repetitive training can be facilitated with assistance from mechanical devices. Thus, we have developed a training environment that integrates augmented reality (AR) with assistive devices for post-stroke hand rehabilitation. The AR element of our environment utilizes head mounted display and virtual objects for reach-and-grasp task training. The assistive device consists of either a body-powered orthosis (BPO) or a pneumatic-powered device (PPD), both of which are incorporated into gloves. This environment can be easily set up and calibrated, is customizable for individual users, and requires active user participation. Additionally, it can be used with both real and virtual objects, as desired. We are currently conducting pilot case studies to assess ease of use and efficacy. At present, one stroke survivor from each of the three training conditions, AR-with-BPO, AR-with-PPD and AR-only (acting as the control), has completed the 6-week training paradigm. Preliminary findings suggest user acceptance of the technology and some potential for beneficial effects.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".