Method of Recognition and Assistance Combining Passive RFID and Electrical Load Analysis That Handles Cognitive Errors
Bibliographic record
Abstract
The integration of wireless sensor technologies has increased awareness of many laboratories on the field of embedded network system. Many researchers seek exploiting these advances to develop technological assistance for frail people in smart homes. However, to reach the full potential of applications using network embedded systems such as assistive smart home, the first challenge to overcome is the recognition of the ongoing inhabitant activity of daily living (ADL). Moreover, to provide adequate assistance, it is essential to be able to detect every perceptive error. Such an approach proposes the use of ubiquitous sensors hidden in the environment for monitoring and detecting behavioral abnormalities associated with cognitive deficits and then does a proper guidance by providing advice using different kinds of effectors (screen, light, sound, etc.). In this paper, we present an affordable system that exploits a combination of passive RFID and the load signatures of appliances to assist elders and to detect errors related to cognitive impairment. The entire multi-sensor system has been implemented and deployed in a real prototype smart home. We present the promising results of our experiment on real daily routines.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".