Mode Detection in switched pursuit tracking tasks: Hybrid estimation to measure performance in Parkinson's disease
Bibliographic record
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder that impairs motor skills, speech, and other voluntary movement, and may be associated with cognitive inflexibility. Fourteen PD subjects (both on and off medication) and 10 normal subjects performed a manual pursuit tracking task, in which the dynamics of the task suddenly change without explicit enunciation. The task dynamics have three modes, in which the error (the difference between the target and the user's cursor) is attenuated, exaggerated, or unchanged - hence we model the subject performing the tracking task as a hybrid system with arbitrary switching. Second-order stochastic LTI models of tracking performance in each mode are first obtained through system identification. We then use a multiple model adaptive estimation (MMAE) algorithm to determine a) whether each subject successfully adapted to the sudden change in tracking dynamics, and if so, b) the delay in switching to the new mode. These parameters were analyzed for all subjects, and found to be statistically significant across groups. While normal subjects consistently detected the change in task dynamics, PD subjects show considerably more difficulty in detecting the switch (especially off medication), and did not switch into the new mode as quickly as normal subjects. Our results suggest that PD subjects have considerable impairment in adapting to changing motor environments.
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 | 0.005 |
| 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".