A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms
Bibliographic record
Abstract
Tele-rehabilitation (Tele-rehab) is changing the landscape of virtual care by redefining assessment and breaking accessibility barriers as a convenient substitute for conventional rehabilitation. The COVID-19 pandemic resulted in a rapid uptake of virtual care. Researchers and health professionals have started developing new tele-rehab platforms, e.g., in the form of video conferencing. Albeit useful, these platforms still require the clinicians’ time and energy. Integrating a biofeedback system that can reliably distinguish between “Correctly Executed” from “Incorrectly Executed” exercises into tele-rehab platforms can help patients to perform rehab exercises correctly, avoid injuries, and enhance recovery. To address this gap, this paper proposes an automated system that uses machine learning to classify correct and incorrect executions of 9 rehabilitation gestures. The model is trained on 24 angle signals extracted from different body sections. The angle signals are obtained in 3D space, and 10 features are extracted from each signal. Six different classifiers, including Random Forest, Multi-Layer Perceptron Artificial Neural Networks, Naïve Bayes, Support Vector Machine, K-Nearest Neighbors, and Logistic Regression, are used, and evaluated with 10-Fold and Leave One Subject Out (LOSO) cross validations. The best classifiers achieved an average accuracy of 89.86% ± 3.38% and F1-Score of 72.84% ± 11.98% for 10-Fold and an average accuracy of 88.21% ± 3.90% and F1-Score of 68.16%±13.28% for LOSO. The proposed system has great potential to be integrated into tele-rehab platforms to help patients perform their exercises reliably. © 2017 Elsevier Inc. All rights reserved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.004 | 0.002 |
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".