In-Home Human Activity Recognition via Kinematics-Focused Multi-Modal Sensor Fusion and Spatio-temporal Neural Architecture
Bibliographic record
Abstract
This study presents a robust Human Activity Recognition (HAR) framework for smart home environments, using multi-modal sensor fusion and advanced spatio-temporal deep learning modeling. The system integrates data from a wrist-worn actigraphy device, a Real-Time Location System (RTLS), and a vision-based activity sensor. A hybrid deep learning model combining Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Neural Structured Learning (NSL), enhanced by an attention mechanism, enables accurate interpretation of complex human activity patterns. The results demonstrate the efficacy of multi-modal sensor fusion and spatio-temporal neural architectures that enhance activity recognition accuracy. The model achieved 95.19% accuracy for routine daily activities, outperforming conventional non-temporal models due to its ability to handle the complexity inherent in spatio-temporal data effectively.
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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".