Hand usage and inter-limb coordination parameters for the bimanual rapid visuomotor task to quantify sensorimotor dysfunction of participants with stroke
Bibliographic record
Abstract
Clinical assessment provides a foundation for all aspects of patient care, assisting diagnosis, prognosis and overall patient care.Stroke can impact a broad range of sensory, motor and cognitive functions, but existing assessment tools tend to be largely subjective in nature and use relatively course rating systems.Thus, in practice the majority of stroke patients follow the same general rehabilitation program, which may not necessarily be optimal for each individual case.The major goal of this research is to develop advanced technologies that provide objective and accurate measurements of stroke-caused impairments and aid clinicians in the planning of individual rehabilitation therapy for stroke patients.The key technology in this research is a KINARM (Kinesiological Instrument for Normal and Altered Reaching Movements) robot that allows for the collection of quantitative measurements of upper limb movements of a subject performing a particular task [1].The apparatus permits planar movements of the upper limb in concert with a 2D virtual reality system that displays hand position and targets in the horizontal workspace.This abstract presents a technique for the evaluation of stroke impairment using statistical and time series analysis of the data collected with the KINARM robot.The data were collected from 154 stroke (91 left-and 63 right-affected) and 262 control participants.Each subject completed a bimanual object hit task.In this task participants are instructed to use their right and left hands (represented as green paddles) to hit red balls that move towards the participant.At the beginning of
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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 | 0.002 |
| 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) | 0.005 | 0.001 |
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".