Bibliographic record
Abstract
Traditional treatments, such as medications and deep brain stimulation, are commonly used to manage tremors. However, these methods often involve risks and side effects, creating a need for non-invasive alternatives. According to control theory, the Internal Model Principle (IMP) can perfectly track or reject known periodic signals. Since tremors consist of three harmonic (sinusoidal) components, IMP is a suitable approach for cancelling them without affecting non-periodic components like voluntary motion. In this work, we propose a control method based on the IMP to suppress tremors while preserving voluntary motion. The method uses three IMP controllers and a proportional (KP) controller to target the harmonic components of the tremor. The controller parameters are calculated offline using the open-loop transfer function and the geometric average of each tremor bandwidth. Simulation results and experimental testing with real patient tremor data demonstrate that the proposed method effectively cancels tremor signals while preserving voluntary motion. Across 10 patient datasets, it achieved a median tremor suppression of 85.3 % and voluntary motion preservation of 85 %. The method has a low computational cost and a simple structure, making it suitable for future use in wearable systems such as tremor suppression gloves.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".