Experimental and computational analysis and testing of wearable hand tremor control orthoses
Bibliographic record
Abstract
Hand tremors are among the most prevalent neurodegenerative movement disorders, causing involuntary upper-limb oscillations that significantly impair patients' quality of life. While medications and therapy provide limited relief, wearable tremor suppression devices offer a promising non-invasive alternative. A hand tremor absorber, typically passive or active, is designed to counteract involuntary shaking through mechanical or electronic means. The importance of the proposed design lies in its ability to deliver high-performance, multi-axial tremor suppression without motors, power sources, or restrictive bracing, addressing critical gaps in comfort, wearability, and real-world usability that limit existing solutions. This paper presents the analysis and optimisation of a novel passive, omnidirectional hand tremor absorber that achieves substantial amplitude reduction while preserving natural hand motion. Using a full-scale mannequin arm tremor simulator and MATLAB-based parametric modelling (MathWorks Inc., Natick, MA), key design parameters were optimised across the clinically relevant 3-7 Hz frequency range. Results demonstrate up to 79% unidirectional and 73% omnidirectional tremor suppression. A compact, donut-shaped orthosis integrating dual perpendicular absorbers was developed to effectively dampen complex, multi-directional tremors, achieving ∼75% reduction in severe cases with a total device weight of only 330 g. By combining passive operation, lightweight ergonomics, and multi-axis efficacy, this design offers a practical, patient-centered solution that overcomes the bulk, cost, and invasiveness of current alternatives. Future work will validate these results in human trials to assess real-world impact on functional independence and quality of life.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".