Human activity recognition in smart homes: Combining passive RFID and load signatures of electrical devices
Bibliographic record
Abstract
Modern societies are facing an important aging of their population, leading to rising economic and social 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 to help support the needs of elders. It aims to provide cognitive assistance by taking decisions, such as giving hints, suggestions and reminders, with different kinds of effectors (light, sound, screen, etc.) to a resident suffering from cognitive deficits in order to foster their autonomy. To implement such a technology, the first challenge we need to overcome is the recognition of the ongoing inhabitant activity of daily living (ADL). Moreover, to assist them correctly, we also need to be able to detect the perceptive errors they perform. Therefore, we present in this paper a new affordable activity recognition system, based on passive RFID technology and load signatures of appliances, able to detect errors related to cognitive impairment. The whole computational intelligent system is based on a multi-layer model that promotes scalability and adaptability. This system has been implemented and deployed in a real smart home prototype. We also present the promising results of our experiment conducted on real case scenarios about morning 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) | 0.000 | 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 | 0.000 |
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 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".