Discovery of topological relations for spatial 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) is a challenging problem that could enable an outstanding number of applications in pervasive computing. Many approaches have been developed to overcome this issue, but they all suffer from major drawbacks. While some use invasive sensors such as video-cameras and wearable technology, other exploit complex models to only recognize coarse-grained activities. In this paper, we propose to exploit the largely neglected spatial aspects in the smart home to recognize the activity of daily living (ADLs) of a resident in a noninvasive fashion. To do so, we designed an extension to well-known data mining algorithms that we exploit to automatically learn the models of the resident ADLs. The models are built from the retrieval of spatial patterns corresponding to the topological relationships of the smart home entities. We demonstrate the advantages of our new semi-supervised system through comprehensive experiments inside a smart home and compare the results with expert defined models of activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 it