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Ablation study – impact of model components
| Configuration | Accuracy (%) | F1-score (%) |
|---|---|---|
| SAE only (no attention) | 93.10 | 91.90 |
| SAE + single-head attention | 95.30 | 94.40 |
| Proposed study | 97.82 | 96.67 |
j_ijssis-2026-0024_tab_006
| Algorithm: MHSA-SAE |
|---|
| Input: X ∈ ℝn×t×d |
| Output: Y ∈ ℝn×c |
| 1. Preprocess: |
| X ← Normalize(X) |
| 2. Encode: |
| H ← fSAE (X) |
| 3. Apply Attention: |
| Q, K, V ← Linear(H) |
| A ← MHSA(Q, K, V) |
| 4. Classify: |
| Y ← Softmax (Wc · A+bc) |
| Return Y |
Comparison with existing methods
| Model/Methodology | Accuracy (%) | F1-score (%) | AUC (%) |
|---|---|---|---|
| Chong et al. [15] - Feature selection + SVM | 91.80 | 90.50 | 92.30 |
| Jeong et al. [12] – DL with noninvasive biomarkers | 93.20 | 92.70 | 93.90 |
| Dooley et al. [13] - MASH harmonization with wearables | 94.60 | 93.80 | 95.10 |
| Proposed MHSA-SAE (Ours) | 97.82 | 96.67 | 98.10 |
Performance of MHSA-SAE on test set
| Metric | Value (%) |
|---|---|
| Accuracy | 97.82 |
| Precision | 96.45 |
| Recall | 96.90 |
| F1-score | 96.67 |
| AUC | 98.10 |
Class-wise precision, recall, and F1-score
| Activity Class | Precision (%) | Recall (%) | F1-Score (%) |
|---|---|---|---|
| Standing | 98.1 | 98.4 | 98.2 |
| Walking | 97.3 | 96.9 | 97.1 |
| Running | 95.0 | 94.6 | 94.8 |
| Sitting | 96.7 | 97.0 | 96.8 |
| Lying down | 94.5 | 95.3 | 94.9 |
| Climbing stairs | 93.1 | 92.7 | 92.9 |
Training vs_ validation accuracy over epochs
| Epoch | Training accuracy (%) | Validation accuracy (%) |
|---|---|---|
| 10 | 84.60 | 82.30 |
| 30 | 91.20 | 89.70 |
| 60 | 96.10 | 94.80 |
| 90 | 97.80 | 97.20 |
| 100 | 98.00 | 97.40 |