Assistance in smart homes: Combining passive RFID localization and load signatures of electrical devices
Bibliographic record
Abstract
Most industrialized countries are facing an important aging of their population. To meet needs of seniors and meet economic and sociological challenges such as the pressure on health support services for semi-autonomous persons, smart home technology is considered by many researchers as a promising potential solution. This approach proposes to use ubiquitous sensors hidden in the environment for monitoring and detecting behavioral abnormalities associated with cognitive deficits and then do a proper guidance by giving hints using many kinds of effectors (light, sound, screen, etc.). In a smart environment, assistive system is an important technology in order to fulfill the need to provide support in living environment for people with cognitive deficit. In this paper, we present a new affordable multi-agents assistance system who exploits qualitative spatial reasoning from a combination of RFID localization of everyday life objects and load signature of appliances. We also present promising results of experiments conducted on this new assistance system with real case scenarios of daily living.
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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 | 0.000 |
| 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".