Analysis of the Effect of Common Disturbances on the Safety of a Wearable Tremor Suppression Device
Bibliographic record
Abstract
The advent of wearable technology has enabled a large number of externally worn mechatronic devices to be developed and tested on people with movement disorders. The complexity of these disorders and the variety of conditions across different patients have resulted in a pressing demand for the incorporation of intelligent control systems, especially for a wearable tremor suppression device (WTSD) that can suppress tremor without impeding the user's voluntary motion. Several devices have been developed to reduce tremor; however, the evaluations of these devices have only been done in a controlled lab setting, while the functionality and ability to avoid user injury under the effect of disturbances during daily use have not been investigated. In this study, the performance of a WTSD was tested with several commonly used tremor suppression control systems, i.e., Weight-frequency Fourier Linear Combiner (WFLC), Bandlimited Multiple Fourier Linear Combiner, and enhanced High-order WFLC-based Kalman Filter, on a bench-top tremor simulator. These systems were also tested under the influence of three simulated disturbances that are commonly seen in real life, i.e., data mutation, sensor drift, and measurement loss. The experimental evaluation showed that none of these systems are safe under the disturbances. The tremor power suppression ratio (67.8%-94.2%) of the WTSD was not significantly lowered by the disturbances; however, the error when tracking voluntary motion significantly increased by 8.8°-93.6°, which may present a safety hazard to the users. The results of this study emphasize the importance of integrating safety measures into intelligent WTSDs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".