Development of a Wearable Tremor Suppression Glove
Bibliographic record
Abstract
Current treatments for parkinsonian tremor, such as medication and brain surgery, have shown varying levels of effectiveness and carry the risk of significant side effects and complications. Studies on wearable tremor suppression devices have shown positive results in the use of mechanical and electrical suppression on tremor management of the upper limbs. Wearable technology for tremor suppression is a promising solution for patients who do not respond to medication and do not present severe enough symptoms to undergo surgery. Available tremor suppression devices are mainly for elbow and wrist tremor. Devices for finger tremor suppression have not been developed despite the fact that finger tremor is also present. In this study, a wearable tremor suppression glove prototype was designed and validated with recorded tremor data from patients with Parkinson's disease. Two validation experiments were conducted to assess the performance of the proposed device when suppressing tremor motion and following voluntary motion. The tremor suppression assessment showed an overall tremor amplitude reduction of 85.0% ± 8.1%, and the power reductions for the 1st, 2nd, and 3rd harmonics are 87.9% ± 13.6%, 92.0% ± 7.4%, and 81.7% ± 13.0%, respectively. Following voluntary motion was possible with a RMSE of 14.2% ± 2.5% and a correlation coefficient of 0.97 ± 0.01. Both assessments have shown positive results for the validation of the proposed device; however, further work is needed to improve the performance of the proposed device prior to human trials.
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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 | 0.001 | 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 | 0.000 |
| 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".