A uniform approach to HAR recognition in unobtrusive indoor monitoring systems
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
Abstract Human Activity Recognition (HAR) allows for unobtrusive indoor monitoring, particularly in elderly care. However, existing HAR methods face significant challenges due to the variability in home layouts, sensor types, and activity labels across different datasets, which limits their generalization and scalability. Most approaches require extensive customization, making cross-environment HAR implementation challenging in real-world scenarios. To address these challenges, we propose a unified HAR framework that introduces Functional Areas, which abstract physical spaces into standardized activity zones, and Detector Units, which map heterogeneous sensor configurations into a common representation. We evaluate our framework using multiple publicly available HAR datasets based on ambient sensor data of smart homes, testing two model architectures: a Holistic Approach, which trains a single GRU-based neural network on the combined datasets, and a Reductionist Approach, which employs an ensemble bagging method. The Holistic Approach demonstrated superior generalisation, achieving 0.84 precision and 0.73 accuracy, outperforming the reductionist approach.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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