Tremor suppression orthoses for parkinson’s patients: A frequency range perspective
Bibliographic record
Abstract
While the majority of tremor-afflicted Parkinso-nian (PD) patients suffer from rest tremors, which is not considered highly disabling, a portion of these PD patients also demonstrate action tremors that interfere with their daily lives. Two main considerations in designing an orthosis that aims at suppressing the tremor, are the frequency bands of the tremor and the joints tremor affects. Nine subjects, which included six healthy people, two PD patients with typical tremor afflictions, and a PD patient with severe tremor of not only in her fingers and wrist, but also in her elbow, participated in this study. The highly afflicted patient displayed the need for tremor suppression in action as well as when in rest. The study focuses on uncommon elbow tremors and demonstrates that, for typically afflicted patients, tremor amplitudes are comparable to healthy subjects, but the frequency distribution of the tremors are different at high levels of elbow torque. For the highly afflicted patient, both tremor amplitude and its frequency distribution are different at all levels of elbow torque. The study further investigates the tremors in two bands of frequency on both hands of the highly troubled patient before, and after medication. The two bands are those of classical Parkinsonian tremor (4-6 Hz) and physiological (or enhanced physiological) tremor (8-12 Hz). Power spectrum and tremor amplitude comparisons reveal that, for part of tremulous PD patients, both tremors coexist and, depending on the level of affliction, the designed orthosis needs to suppress tremors in both bands, even at more proximal joints, such as the elbow.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".