Accurate passive RFID localization system for smart homes
Bibliographic record
Abstract
The smart home paradigm is a promising new trend of research aiming to propose an alternative to postpone the institutionalization of cognitively-impaired silver-aged people. These habitats are intended to provide security, guidance and direct support services to its resident. To be able to fulfill this important mission, a smart home system first has to identify the ongoing activities of its user by tracking, in real time, the position of the main daily living objects. Many researchers addressed this issue by proposing systems based on ultrasonic wave sensors, video cameras, and radio-frequency identification (RFID). However, because of its robustness and its low price, RFID constitutes the most viable technology for smart homes. Recently, several RFID localization algorithms have been developed, mainly for commercial and industrial uses, but they are not precise enough to be used in an assistive recognition context or they focus on active tags, which need batteries and are much more expensive. We present, in this paper, a new algorithmic approach for passive RFID localization in smart homes based on elliptical trilateration and fuzzy logic. This new algorithm has been implemented in a real smart home infrastructure and has been rigorously tested. We also analyze and compare the obtained results with the main existing approaches.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".