Development and Validation of a Finger Tremor Simulator
Bibliographic record
Abstract
Tremor, being one of the most severe symptoms of Parkinson's disease, has been considered as not only a medical problem but also an engineering problem. Increasingly, wearable technologies are being considered as a viable treatment option. In order to study and control tremor in the field of engineering, the first step often includes modeling and simulation, as access to patients is limited. With the successful realization of a finger tremor simulator, a wearable tremor suppression device could be validated prior to testing on humans. In this study, a tremor simulator was designed and validated with recorded patient tremor data. Two experimental assessments were conducted on the validation of tremor motion reproduction and tremor torque reproduction. The results showed that the proposed simulator has 9%, 82%, and 141% error in the reproduction of the power of the 1st, 2nd and 3rd harmonics of the tremor, and 11.89% mean error on motion reproduction. The tremor torque measured at the index finger metacarpophalangeal joint is 0.02± 0.02 Nm, and the output torque from the tremor simulator is 0.03 ±0.01 Nm. Further parameter adjustment of the control system is required to improve performance.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".