A Frequent Pattern Mining Approach for ADLs Recognition in Smart Environments
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Bibliographic record
Abstract
This paper presents an approach for recognition of Activities of Daily Living (ADLs) in smart environments. Our approach is based on the frequent pattern mining principle to extract frequent patterns in the datasets collected from different sensors disseminated in a smart environment. In contrast with existing intrusive activity recognition approaches that have been proposed in the literature, where the datasets are basically composed of audio-visual or images files recorded during experiments, our approach is fully non-intrusive and it is based on the analysis of event sequences collected from heterogenous sensors. Our approach consists of two main phases, (1) frequent pattern mining to extract frequent patterns, and (2) activity recognition using a mapping function between the extracted frequent patterns and the activity models. We show through experiments how our approach accurately recognizes tasks as well as activities and outperforms the HMM model.
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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 | 0.000 |
| 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) | 0.000 | 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 it