Anomaly Detection Technologies for Dementia Care: Monitoring Goals, Sensor Applications, and Trade-offs in Home-Based Solutions—A Narrative Review
Bibliographic record
Abstract
Anomaly detection technologies are increasingly used to monitor people living with dementia (PLwD) in home settings, addressing critical behaviors such as wandering, sleep disturbances, and agitation. This narrative review examines technologies used for detecting behavioral anomalies, the activities they monitor, and the trade-offs between their benefits and limitations. A systematic search across MEDLINE, IEEE Xplore, ACM Digital Library, and Web of Science identified 78 studies, categorized through thematic analysis. Three primary motivations emerged: early diagnosis, safety monitoring, and reducing caregiver stress while promoting autonomy. Technologies include GPS tracking, wearables, environmental sensors, and smart home systems, each with benefits like real-time alerts and non-intrusive monitoring but also challenges such as user compliance, false positives, and privacy concerns. While these systems enhance safety and autonomy, improving sensor accuracy, integrating AI for personalized interventions, and addressing ethical concerns are essential for long-term effectiveness and supporting the well-being of both PLwD and caregivers.
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 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 | 0.001 | 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 | 0.000 |
| 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".