A Real-Time System for Monitoring and Managing Neuropsychiatric Symptoms in Dementia Patients
Bibliographic record
Abstract
Dementia is a range of neurological disorders that affect the brain and are associated with neuropsychiatric symptoms (NPS). Among these symptoms, agitation and aggressive behavior are common, as they can significantly impact an individual's quality of life. Monitoring NPS using wearable sensors can help detect the behavior of people with dementia (PwD). This paper proposes a real-time remote healthcare monitoring system with scalable event processing and analytics platforms for healthcare applications using open-source components. We studied the system with sample data from PwD at the Ontario Shores Mental Health Institute. The system can collect data from wearable devices such as wristbands, rings, or patches and uses machine learning to classify agitation in PwD. The proposed system also provides valuable insights into other health problems. Moreover, it handles continuous data in real-time while performing classification with high accuracy using the Extra Trees model. Furthermore, we design the system featuring a horizontally scalable architecture to adapt to the growing number of devices from different sensors over time. This research introduces an end-to-end real-time system capable of rapidly identifying aggressive behavior and promptly notifying healthcare providers in 3.5 seconds using a customized mobile app and dashboard. The results highlight the potential of this system in enhancing the early detection of NPS in PwD and ultimately reducing the risks faced by PwD and caregivers.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".