Admittance-Based Voluntary-Driven Motion With Speed-Controlled Tremor Rejection
Bibliographic record
Abstract
In the elderly, there have been reports of above 10% of the population being diagnosed with pathological tremor. Some patients find the conventional medical solutions, which primarily include medications and surgery, insufficient to treat their conditions. Technologies to mechanically suppress tremor have been proposed as a potential alternative. Robotic solutions for suppressing human tremor typically revolve around identifying and isolating the tremor motion component. This study proposes instead to only estimate the intentional movement and reject any disturbance that interferes with such an intended movement. By designing the control feedback such that motions other than the voluntary are rejected, the tremor component is attenuated. The controller components include an outer-loop admittance feedback loop and an internal velocity feedback loop. Additionally, a state feedback is utilized to improve the velocity tracking. To test the proposed approach, a benchtop tremor simulation device (TSD) was developed. The TSD consists of two motors, which, respectively, simulate the motion of a human joint and the suppressing action of a mechanical suppression system. A parametric stability analysis of the proposed tremor-suppression strategy is presented. A motion profile recorded from a person with tremor is used as human input in the TSD to experimentally validate the proposed tremor rejection strategy. Spectral analysis results show a 99.8% tremor reduction; the power reduction related to the voluntary movement is instead negligible (0.18%). Tracking of a velocity profile results in a small root-mean-square (rms) error (0.58 rad/s). The paper concludes by comparing the proposed approach to others presented in the literature.
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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 | 0.000 |
| 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 | 0.000 | 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".