Assessment of a Fault-Tolerant Control-Based Wearable Tremor Suppression Glove Under Faults and Disturbances
Bibliographic record
Abstract
Pathological tremor severely impacts the quality of life of affected individuals. The need for tremor management approaches that are free of side effects and surgical complications has sparked research in wearable tremor suppression technology. The existing wearable tremor suppression devices have achieved suppression ratios of up to 90%. Although the achieved performance is promising, the safety of using these devices outside of a lab environment, where faults and disturbances exist, has not been studied. It was recently discovered that existing tremor suppression systems are not effective and safe for users when faults and disturbances are present. Therefore, this study proposes and evaluates a novel fault-tolerant control system for tremor suppression. Using 18 tremor datasets previously recorded, the performance of the proposed system under three simulated common faults was evaluated on a bench-top mechatronic tremor simulator. The assessment showed that the proposed system remained safe and functional after introducing the faults, maintaining at least a 60% tremor suppression rate, and root mean square tracking error lower than 2.7° (compared to 80.5° without the proposed system). This study improves the robustness and safety of wearable tremor suppression devices, providing strong evidence to facilitate the transition of these devices from the lab to real-life applications.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".