Application of Modified PageRank Algorithm for Anomaly Detection in Movements of Older Adults
Bibliographic record
Abstract
It is a well-known statistic that the percentage of our older adult population will globally surpass the other age groups. A majority of the elderly would still prefer to keep an active life style. In support of this life style, various monitoring systems are being designed and deployed to have a seamless integration with the daily living activities of the older adults while preserving various levels of their privacy. Motion tracking is one of these health monitoring systems. When properly designed, deployed, integrated, and analyzed, they can be used to assist in determining some onsets of anomalies in the health of elderly at various levels of their Movements and Activities of Daily Living (MADL). This paper explores how the framework of the PageRank algorithm can be extended for monitoring the global movement patterns of older adults at their place of residence. Through utilization of an existing dataset, the paper shows how the movement patterns between various rooms can be represented as a directed graph with weighted edges. To demonstrate how PageRank can be utilized, a base graph representing a normal pattern can be defined as what can be used for further anomaly detection (e.g., at some instances of observation the measured movement pattern deviates from what is previously defined as a normal pattern). It is shown how the PageRank algorithm can detect simulated change in the pattern of motion when compared with the base-line normal pattern. This feature can offer a practical approach for detecting anomalies in movement patterns associated with older adults in their own place of residence and in support of aging in place paradigm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".