A Novel and Distributed Approach for Activity Recognition Inside Smart Homes
Bibliographic record
Abstract
The human life expectancy has never stopped increasing during the last decades causing, among other benefic phenomenon, the apparition of different types of dementia often due to the Alzheimer disease. Despite a reduced independence these patients want to stay at their home more than anything causing the apparition of an expensive home care. To answer this problem, smart homes appeared, and many works focused on recognize the activities made by the inhabitant to assist him if the need arises. However, only a few authors worked on creating a cheap and reliable smart home infrastructure. We already addressed this point in previous works by creating a distributed architecture. Still, we did not prove, at the time, that the activity recognition was still achievable on such infrastructure. Consequently, we present, in this paper, a new kind of distributed activity recognition implementing a form of safety based on reflex behavior and making all its decision in a distributed manner. With an average accuracy at least as good as the centralized manner, we prove here that even if the architecture is distributed, the activities can still be recognized.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".