The Use of Machine Learning and Deep Learning Techniques to Assess Proprioceptive Impairments of the Upper Limb after Stroke
Bibliographic record
Abstract
Abstract Background Proprioception is commonly impaired after stroke. Robotic tools precisely measure multiple attributes of position sense and create large datasets. Previously, we quantified individual performance based on single measured robotic parameters and an overall task score in an arm position matching (APM) task. In the present manuscript, we used machine learning and deep learning techniques to classify whether individuals had a stroke or not based on their robotic APM task performance. Methods Participants performed an APM task in the Kinarm exoskeleton robot that produced 12 parameters to quantify multiple attributes of position sense. We first quantified impairment in individual parameters and overall task score by determining if participants with stroke fell outside of the 95% cut-off score of control (normative) values. Then, we applied five machine learning algorithms: Logistic Regression, Decision Tree, Random Forest, Random Forest with Hyperparameters Tuning, and Support Vector Machine; and a deep learning algorithm: Deep Neural Network, to classify individual participants as to whether or not they had a stroke based only on robotic assessment results using a 10-fold cross-validation approach. Results We recruited 429 participants with neuroimaging confirmed stroke (< 35 days post-stroke) and 465 healthy controls. Depending on the APM parameter, we observed that 10.9–48.4% of stroke participants were impaired. Using the overall task score, 44% were impaired. The mean performance metrics of machine learning and deep learning models were: accuracy 82.4%, precision 85.6%, recall 76.5%, and F1 score 80.6%. All machine learning and deep learning models displayed similar classification accuracy; however, the Random Forest model had the highest numerical accuracy (83%). Our models showed high sensitivity and specificity (AUC = 0.89) in classifying individual participants based on their performance in the APM task. We also found that variability was the most important feature of classifying performance in the APM task. Conclusion Our machine learning and deep learning models displayed similar classification accuracy. Each model classified more participants correctly as stroke or control than classification of impairment based on individual parameters or overall task score using a cut-off score. Machine learning and deep learning techniques may provide opportunities to better understand proprioceptive impairments after stroke.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".