Bibliographic record
Abstract
Being able to monitor movements and activities of the elderly in a smart living environment can offer an approach for detecting any on-set of anomalies. These smart living dwellings can be equipped with various networked ambient sensors, wearable sensing technologies and cloud-robotics. Detection of any such on-set of anomalies in elderly movements and activities can further be used to determine the state of mental health of the elderly for example related to dementia or Alzheimer. There are two mains challenges associated with the deployment such sensor network in the dwelling of elderly. The main challenge is in the processing of the sensed information in order to ensure the privacy of individuals. The next big challenge is the robustness of tracking algorithm in the presence of lost or occluded sensed data. It has been shown that the recursive Bayesian approach can offer a suitable framework for tracking targets with the maneuvering trajectories which follows a non-linear behavior and non-Gaussian distribution. This paper presents an overview of recursive Bayesian framework which can be used as a part of tracking environment in the smart living environment of elderly. Through step-by-step development, the paper highlights various features of the method which can be adapted in order to reduce various computational issues associated with the implementation of this framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".