Investigation of the Kinematic Upper-Limb Movement Assessment (KUMA): A Pilot Study
Bibliographic record
Abstract
Purpose: Upper limb movement disorders are common after stroke and can severely impact activities of daily living. Available clinical measures of these disorders are subjective and may lack the sensitivity needed to track a patient’s progress and to compare different therapies. Kinematic analyses can provide clinicians with more objective measures for evaluating the effects of rehabilitation. We present a novel method to assess the quality of upper limb movement: the Kinematic Upper-limb Movement Assessment (KUMA). This assessment uses motion capture to provide three kinematic measures of upper limb movement: active range of motion, speed, and compensatory trunk movement. The researchers sought to evaluate the ability of the KUMA to distinguish motion in the affected versus unaffected limb. Method: We used the KUMA with three participants with stroke to assess three single-joint movements in: wrist flexion and extension, elbow flexion and extension, and shoulder flexion/extension and abduction/adduction. Participants also completed the Modified Ashworth Scale and the Chedoke-McMaster Stroke Assessment, two clinical measures of functional ability. Results: The KUMA distinguished between affected and unaffected upper limb motion. Conclusions: The KUMA provides clinicians with supplementary objective information for motion characterization that is not available through clinical measures alone. The KUMA can complement existing clinical measures such as the MAS and CMSA and can be helpful for monitoring patient progress.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".