Switched manual pursuit tracking to measure motor performance in Parkinson's disease
Bibliographic record
Abstract
Control theoretic measures are proposed to assess motor performance in Parkinson's disease (PD), a neuro-degenerative disorder that impairs motor skills, speech, and aspects of cognition. Ten normal and 14 PD subjects performed a series of manual pursuit tracking tasks: three tasks were first performed separately, then as a merged sequence with sudden, unenunciated task changes. The tasks differed in whether the tracking errors appeared amplified, attenuated or unaltered. From the discrete block experiments, subject- and task-specific second-order, linear time invariant models were derived, with the trajectory subjects are asked to track as input and the subject's motor response as output. Multiple model adaptive estimation was employed on the merged sequences to determine whether, and with what delay, each subject modified their performance after a task change. Although all normal subjects detected the task change, less than one-third of PD subjects did (and with longer delay). Further, those PD subjects who detected the task change had estimators with higher damping ratio than those PD subjects who did not. Since cerebellar structures may affect damping ratio, and the basal ganglia are often associated with switching behaviour, the proposed method provides a comprehensive assessment of motor structures that may be affected in PD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".