Deep Learning Classification of Knee Osteoarthritis Severity from EMG Signals Using LSTM Model
Bibliographic record
Abstract
Knee osteoarthritis (KOA) is commonly diagnosed by medical experts, which may lead to variability. This study aims to develop a deep learning approach using Long Short-Term Memory (LSTM) to classify KOA severity based on surface EMG signals. Methods: EMG signals from medial gastrocnemius (MG) and vastus medialis (VM) muscles were segmented into fixed-length sequences and processed using a two-layer LSTM network with dropout and softmax output. Models were trained separately for MG and VM datasets with categorical cross-entropy and Adam optimizer. Results: MG-based model achieved F1-score 0.9051, while VM-based model showed F1-score 0.8131. Confusion matrix indicates robust classification across three KOA levels. Conclusion: The LSTM model effectively classifies KOA severity from EMG data, highlighting its potential as a non-invasive diagnostic tool. Practical implications: This method can support clinicians and integrate into real-time EMG-based KOA assessment systems.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".