Haptic-Enabled Hand Rehabilitation in Stroke Patients: A Scoping Review
Bibliographic record
Abstract
There is a plethora of technology-assisted interventions for hand therapy, however, less is known about the effectiveness of these interventions. This scoping review aims to explore studies about technology-assisted interventions targeting hand rehabilitation to identify the most effective interventions. It is expected that multifaceted interventions targeting hand rehabilitation are more efficient therapeutic approaches than mono-interventions. The scoping review will aim to map the existing haptic-enabled interventions for upper limb rehabilitation and investigates their effects on motor and functional recovery in patients with stroke. The methodology used in this review is based on the Arksey and O’Malley framework, which includes the following stages: identifying the research question, identifying relevant studies, study selection, charting the data, and collating, summarizing, and reporting the results. Results show that using three or four different technologies was more positive than using two technologies (one technology + haptics). In particular, when standardized as a percentage of outcomes, the combination of three technologies showed better results than the combination of haptics with one technology or with three other technologies. To conclude, this study portrayed haptic-enabled rehabilitation approaches that could help therapists decide which technology-enabled hand therapy approach is best suited to their needs. Those seeking to undertake research and development anticipate further opportunities to develop haptic-enabled hand telerehabilitation platforms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".