Comprehensive Kinematic Model of a Tendon-Driven Wearable Tremor Suppression Device
Bibliographic record
Abstract
Wearable devices can suppress or reduce hand tremor motion associated with neurological disorders such as Parkinson's disease. Tendon-driven transmission systems have been proposed as a way to decrease the size and weight of these devices; however, they have complex control system requirements due to their substantially nonlinear behavior. To address this issue, this study focused on the development of a comprehensive kinematic model of a wearable tremor suppression glove that more accurately calculates the tendon displacement during hand motion. The novelty lies in the identification of the threshold bending angle for each joint at which the driven tendon touches the arc of the joint. The derived kinematic model of the glove was verified by both simulation and benchtop experiments, and the proposed model was validated during single-joint and multijoint hand movements. The kinematic model shows a mean 2-D correlation coefficient of 0.96 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\boldsymbol{\pm }$</tex-math></inline-formula> 0.01 with the experimental data. Compared to the Euclidean norm model presented in the literature, it presents an average 83% improvement (a 4%–96% reduction in root mean square errors depending on the joint), which is most significant for increasing tendon displacements.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".