Chronic Stroke Survivors Improve Reaching Accuracy by Reducing Movement Variability at the Trained Movement Speed
Bibliographic record
Abstract
BACKGROUND: Recovery from stroke is often said to have "plateaued" after 6 to 12 months. Yet training can still improve performance even in the chronic phase. Here we investigate the biomechanics of accuracy improvements during a reaching task and test whether they are affected by the speed at which movements are practiced. METHOD: We trained 36 chronic stroke survivors (57.5 years, SD ± 11.5; 10 females) over 4 consecutive days to improve endpoint accuracy in an arm-reaching task (420 repetitions/day). Half of the group trained using fast movements and the other half slow movements. The trunk was constrained allowing only shoulder and elbow movement for task performance. RESULTS: Before training, movements were variable, tended to undershoot the target, and terminated in contralateral workspace (flexion bias). Both groups improved movement accuracy by reducing trial-to-trial variability; however, change in endpoint bias (systematic error) was not significant. Improvements were greatest at the trained movement speed and generalized to other speeds in the fast training group. Small but significant improvements were observed in clinical measures in the fast training group. CONCLUSIONS: The reduction in trial-to-trial variability without an alteration to endpoint bias suggests that improvements are achieved by better control over motor commands within the existing repertoire. Thus, 4 days' training allows stroke survivors to improve movements that they can already make. Whether new movement patterns can be acquired in the chronic phase will need to be tested in longer term studies. We recommend that training needs to be performed at slow and fast movement speeds to enhance generalization.
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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 | 0.000 | 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) | 0.003 | 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".