Object-directed actions in Parkinson’s disease: Observation and imitation of hand movements
Bibliographic record
Abstract
Action observation and imitation may facilitate movement and increase functional independence in Parkinson’s disease (PD). People with PD have been found to imitate intransitive actions in a similar manner to neurologically healthy older adults, but their ability to imitate object-directed hand movements has not been investigated using kinematic measures. The present study examined observation and imitation of object-directed hand movements in 18 participants with PD compared to 21 neurologically healthy age-matched control participants. Participants observed and immediately imitated sequences showing a human hand reaching for and transferring an object between horizontal positions. Both participants with PD and age-matched controls significantly modulated the vertical amplitude of their finger movements when imitating elevated vs. direct trajectories. Movements were lower in vertical amplitude and faster when imitating the reaching segment of the observed movement than the transfer segment. Eye-tracking revealed significant effects of predictability of the movement sequence on saccade amplitude in the control group, specifically when observing elevated movements, but no effects of predictability on eye movements were found for the PD group. This study provides quantitative evidence indicating that people with mild to moderate PD can imitate the kinematics of object-directed hand movements, although their observation and prediction of such movements may be somewhat altered. These findings suggest that interventions based on observation of object-directed actions may facilitate daily activities in PD through imitation of movement kinematics.
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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 | 0.000 | 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".