Real-Time Activity Prediction and Recognition in Smart Homes by Formal Concept Analysis
Bibliographic record
Abstract
In this paper, we introduce a new knowledge-driven approach based on the Formal Concept Analysis (FCA) to predict and recognize Activities of Daily Living (ADLs) in ubiquitous computing environments, in order to duly provide continuous assistance for residents. The proposed approach constructs an incremental inference engine and achieves progressive deductive reasoning to recognize an unfinished ongoing ADL in real-time. For the purpose of finding out the most probable ongoing activity among possible candidates, we propose an assessment based on the root-mean-square deviation (RMSD) to evaluate the relevance of each intermediate prediction. Besides the on-the-fly recognition mode, our approach also possesses high discrimination in differentiating derived and similar activities. Excellent recognition results (almost 100 %) and high prediction accuracies (more than 70 %) are obtained in the experiments.
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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 | 0.003 |
| 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".