Activity recognition in smart homes based on electrical devices identification
Bibliographic record
Abstract
Activity recognition constitutes the key challenge in the development of smart home assistive systems. In this paper, we propose a new algorithmic method for activity recognition in a smart home, based on load signatures of appliances. Most recognition approaches rely on distributed and heterogeneous sensors (ex. RFID), which are intrusive require complex installation, deployment and maintenance. On the other hand, most applications of appliance load monitoring (signal analysis) refer to the energy saving and the costs reducing of energy consumption. Consequently, our proposal constitutes an original application and new algorithmic method based on steady-state operations and signatures. The extraction process of load signatures of appliances is carried out in a three-dimensional space through a single power analyzer, which is non-intrusive (NIALM). We have rigorously tested this new approach by conducting an experiment in our smart home prototype by simulating daily scenarios taken from clinical trials previously done with Alzheimer patients. The promising results we obtained are presented and compared to other approaches, showing that, with an exceptionally minimal investment and the exploitation of relatively limited data, our method can efficiently recognize activities of daily living for providing assistive services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".