Knee Joint Health Care Monitoring System using AI and IoT - Classification Approach
Bibliographic record
Abstract
Knee osteoarthritis is a significant global health concern and the third major problem in India. Effective diagnosis and monitoring of knee joint health are crucial, and Vibroarthrography (VAG) has been adopted as a diagnostic procedure. Vibroarthrography (VAG) signal denoising is done by using Variational Mode Decomposition (VMD). The work investigates the effectiveness of VMD in separating a signal into its intrinsic components, followed by the identification and removal of noise-dominated modes. Various time-domain, frequency-domain, and spectral features are extracted from the decomposed modes and the reconstructed signal to gain insights into the signal characteristics. Machine learning classifiers are then utilized to categorize the signals. Our results indicate that ensemble classifiers, specifically the Voting Classifier and AdaBoost, achieved an accuracy of 93% in distinguishing between normal and abnormal VAG signals.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".