GRU-Based Multi-Modal Human Activity Recognition
Why this work is in the frame
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Bibliographic record
Abstract
Human Activity Recognition (HAR) is an important challenge faced in the healthcare industry, particularly in nursing homes for the elderly and vulnerable patients. This study explores a deep learning-based approach for HAR using multi-modal data, specifically skeletal and inertial data. We employ a Gated Recurrent Unit (GRU)-based architecture to classify human activities, using data preprocessing techniques and feature engineering. The proposed model integrates features from both skeletal and inertial sensors to predict activity labels. The final model achieves an accuracy score of 96.30% outperforming previous random forest model which has an accuracy of 90%. The findings highlight the potential of using GRU networks for real-time HAR systems and provide insights into improving classification in multi-modal activity recognition.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| 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