A Novel Robotic Task for Assessing Impairments in Bimanual Coordination Post-Stroke
Bibliographic record
Abstract
Background: Bimanual tasks are integral to the performance of many activities of daily living, but impairments in bimanual coordination following stroke are not well quantified with existing clinical tools. Objective: The current study outlines a novel robotic task for the objective and quantitative assessment of bimanual impairment following stroke. Methods: We developed a robotic, bimanual assessment task using the KINARM robot. The task involved moving a virtual ball on a bar linking the two hands, to targets displayed using a virtual reality system. Seventy-five healthy control participants and 23 participants with sub-acute stroke were assessed using the task. Task performance of participants with stroke was compared with the healthy control group, as well as to standard clinical tests (Chedoke- McMaster Stroke Assessment (CMSA) arm and hand, Functional Independence Measure (FIM), Montreal Cognitive Assessment (MoCA) and Behavioural Inattention Test (BIT)). Results: A range of impairments in bimanual task performance was found for participants with stroke. As a group, 85% of participants with stroke had impairments on more task parameters than 95% of healthy controls. Participants with stroke commonly displayed impairments in task success (fewer targets hit); movement metrics (slower movement speed) and bimanual coordination (larger difference in reaction time between hands, greater number of speed peaks with unaffected versus affected limb and greater absolute tilt of the bar). Overall performance of the robotic task (total number of parameters ‘failed’) was significantly correlated with motor performance scores (CMSA, r=-0.6) and strongly correlated with measures of functional ability (FIM motor, r=-0.8). Conclusions: A robotic bimanual task can identify impairments in a population of stroke participants and provides a quantitative measure of bimanual coordination.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".