Haptic Feedback Manipulation During Botulinum Toxin Injection Therapy for Focal Hand Dystonia Patients: A Possible New Assistive Strategy
Bibliographic record
Abstract
Abnormality of sensorimotor integration in the basal ganglia and cortex has been reported in the literature for patients with task-specific focal hand dystonia (FHD). In this study, we investigate the effect of manipulation of kinesthetic input in people living with writer's cramp disorder (a major form of FHD). For this purpose, severity of dystonia is studied for 11 participants while the symptoms of seven participants have been tracked during five sessions of assessment and Botulinum toxin injection (BoNT-A) therapy (one of the current suggested therapies for dystonia). BoNT-A therapy is delivered in the first and the third session. The goal is to analyze the effect of haptic manipulation as a potential assistive technique during BoNT-A therapy. The trial includes writing, hovering, and spiral/sinusoidal drawing subtasks. In each session, the subtasks are repeated twice when (a) a participant uses a normal pen, and (b) when the participant uses a robotics-assisted system (supporting the pen) which provides a compliant virtual writing surface and manipulates the kinesthetic sensory input. The results show (p-value using one-sample t-tests) that reducing the writing surface rigidity significantly decreases the severity of dystonia and results in better control of grip pressure (an indicator of dystonic cramping). It is also shown that (p-value based on paired-samples t-test) using the proposed haptic manipulation strategy, it is possible to augment the effectiveness of BoNT-A therapy. The outcome of this study is then used in the design of an actuated pen as a writing-assistance tool that can provide compliant haptic interaction during writing for FHD patients.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".