Classifying Residual Stroke Severity Using Robotics-Assisted Stroke Rehabilitation: Machine Learning Approach
Bibliographic record
Abstract
BACKGROUND Stroke therapy is essential to reduce impairments and improve motor movements by engaging autogenous neuroplasticity. Utilizing machine learning and therapy session kinematic measurements promises to have a central role in rehabilitation decision-making in determining if patient therapy is improving. OBJECTIVE This study uses supervised learning methods to address a clinician’s autonomous classification of stroke residual severity labeled data towards improving in-home robotics-assisted stroke rehabilitation. METHODS Thirty-three stroke patients participate in in-home therapy sessions using the Motus Nova robotics rehabilitation technology to capture upper and lower body motion. The therapy session summary data is based on high-resolution movement and assistance data and clinician-informed discrete stroke residual severity labels. This arises from a final processed dataset of 32,902 patient sessions based on the maximum score per patient per session. Four machine learning algorithms are used to classify stroke residual severity: light gradient boosting, extra trees, deep neural networks, and classical logistic regression. Their performance measures are evaluated to identify which method maximizes stroke residual severity classification accuracy. RESULTS We demonstrate that the light gradient boosting method provides the most reliable autonomous detection of stroke severity. CONCLUSIONS We show how objectively measured rehabilitation training paired with machine learning methods can be used to identify the residual stroke severity class with efforts to enhance in-home self-guided, individualized stroke rehabilitation. As data from rehabilitation practices are often of comparable size and nature to the data collected in our study, this suggests that the light gradient boosting method should be considered a standard, more efficient tool for this analysis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".