Variability of Parkinsonian Tremor During Different Tasks and Under External Interference
Bibliographic record
Abstract
Tremor is one of the earliest signs of Parkinson’s disease (PD) that typically occurs at rest but can also manifest during postural actions or voluntary motion. Several studies have demonstrated that there is great variability in tremor across different body parts, among individuals, and within the same individual across multiple trials. While methods such as functional electrical stimulation (FES) have shown promising results for tremor suppression, the use of these methods relies on the ability to predict and estimate the tremor. This study examines four PD tremor characteristics, including magnitude, power spectrum density (PSD), frequency, and approximate entropy (ApEn), as measures of regularity. These characteristics are measured under different conditions with and without external interference and voluntary movements. Results show that the tremor frequency changes when voluntary motion is involved or when external disturbances such as FES or mechanical loading are used to suppress tremor (${p} \lt {0}.{05}$). Tremor power and magnitude also change in the presence of voluntary motion (${p} \lt {0}.{05}$). On the other hand, ApEn is more consistent in the absence of external interference, independent of voluntary movement, but changes when electrical stimulation or mechanical loading is used to suppress tremor (${p} \lt {0}.{05}$). Gaining a general understanding of tremor variability and changes in tremor characteristics helps enhance wearable tremor suppression devices (WTSDs) to address intraindividual tremor fluctuations effectively.
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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 | 0.002 |
| 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) | 0.000 | 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".