Hybrid CNN-LSTM-GRU with Attention for Human Activity Recognition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Human activity recognition (HAR) using smartphone inertial sensors plays a critical role in mobile health applications through the continuous, unobtrusive monitoring of daily activities. This study introduces a hybrid deep learning architecture combining Convolutional Neural Networks (CNNs) for spatial feature extraction, Long Short-Term Memory networks (LSTMs) and Gated Recurrent Units (GRUs) for capturing complex temporal dependencies and an attention mechanism to dynamically focus on informative time steps. We evaluated this model using the standardized DAGHAR benchmark, specifically on RealWorld waist data collected via smartphones. Our experiments demonstrated a strong average cross-validation accuracy which outperforms the other models as reported in the literature, thus highlighting the model’s potential for mobile health applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 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 it