Multimodal Tremor Suppression of the Wrist Using FES and Electric Motors–A Simulation Study
Bibliographic record
Abstract
Wearable technologies have shown promising results in tremor management, making them a feasible alternative to current treatments. Devices based on active actuation, such as electric motors, show high tremor suppression rate, but are heavy and bulky. In contrast, devices based on functional electrical stimulation (FES) are lightweight and smaller in size, but might cause pain, discomfort, and FES-induced muscle fatigue. Also, when the stimulation parameters do not adapt to the tremor, their suppression performance is compromised. Therefore, a multimodal approach was developed and tested by modeling wrist joint dynamics and simulating the muscle response to FES based on an existing dataset collected from 18 participants with Parkinson's disease. The goal was to evaluate and compare the performance of a multimodal device to that of an FES-only or an electric-motor-only approach. A nonlinear control system allocates the control effort between FES and motor torque based on the tremor level. Results showed an improvement in tremor suppression level (up to 12%) in the multimodal approach compared to FES only when there is voluntary motion. Also, the voluntary motion tracking error was lower in the multimodal approach, compared to the FES-only method (up to 57%). No notable improvement in tremor suppression level or voluntary motion tracking error was observed by comparing the multimodal system and the motor only approach. However, the advantage of using a multimodal system compared to the motor-only system is the reduction of the required motor torque resulting in reduced weight and size of the final device.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".