Towards User Activity Recognition Through Energy Usage Analysis And Complex Event Processing
Bibliographic record
Abstract
One of the key challenges related to the field of Ambient Assisted Living (AAL) is the recognition of the user's activities of daily living. Most existing approaches rely on distributed sensors, such as cameras, RFID and motion sensors. These approaches suffer from high intrusiveness for the resident, coupled with an important amount of hardware that requires maintenance. In this paper, we explore a new, low-cost and efficient solution for fine-grained activity recognition using energy consumption as input. Existing works exploiting energy sensors see the problem from an energy saving and costs reducing point of view; the originality of our work is to characterize a user's actions and activities by decomposing the total power load into a sum of loads for individual appliances. This is done using only the data from a single energy sensor located at the main electrical panel. These contributions have been implemented and tested in real live smart home prototype, using a Complex Event Processing (CEP) engine.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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".