Computational Intelligence Driven Motor Function Assessment in Post-Stroke Patients
Bibliographic record
Abstract
This paper offers an investigation into leveraging computational intelligence (CI) for the assessment of stroke-induced motor weakness in post-stroke survivors, serving as an indicator of neurological function. The proposed methodology deploys deep learning algorithms to analyze video recordings obtained during the post-stroke hospitalization phase. The model effectively categorizes the degree of stroke-induced weak-ness in the patient's left arm across two and three distinct classes aligned with the National Institutes of Health Stroke Scale. This study was motivated by the limitations of existing monitoring technologies, such as using pressure-sensing mattresses (low resolution and low accuracy). Our long-term strategy is to deploy several means for monitoring the patients' motor function. This study demonstrated a binary classification model using video data collected from a cohort of 23 post-stroke patients in a clinical setting for 48 hours. Employing a 3-fold cross-validation methodology, the developed model showcases an accuracy rate of 92.10$\pm$4.08% for the binary classification, distinguishing between mild and severe stroke-induced weakness in the left arm. In the case of three classes, the model achieves an accuracy of 89$\pm$4.95%.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".