Assessing Stroke Patients Movements Using Inertial Measurements Through the Advances of Ensemble Learning Technology
Bibliographic record
Abstract
Internet of things (IoT) and wearable sensors enabled the possibility of measuring activities of daily living (ADLs). The accuracy and precision of detecting stroke patients' movements, using wearable sensors and analyzing data by implementing the advancement of machine learning and ensemble learning technology can enhance the stroke patients' remote assessment techniques. Therefore, this paper aimed to apply ensemble in addition to the machine learning models on the Xsens sensors dataset derived from wearable sensors and collected from twenty stroke survivors. Then, we compare the performance of the single model with the bagging and boosting ensemble learning methods to detect the affected hand of the stroke survivors from the non-affected hand. The results indicated that the ensemble techniques such as GentleBoost and AdaBoost achieved the highest model performance with 91.2% and 89.6% accuracy, respectively in comparison with other single machine learning techniques as well as other ensembles. It was also noted that the subspace ensemble techniques achieved the lowest accuracy, recall, and specificity compared to the Decision Tree.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".