A Novel Multi-Modal Sensor Dataset and Benchmark to Detect Agitation in People Living with Dementia in a Residential Care Setting
Bibliographic record
Abstract
People living with dementia (PwD) in residential care settings often exhibit responsive behaviors, with agitation being the most common behavior. Automated systems to detect agitation events have the potential to improve patient care by helping to track symptoms and their response to interventions. We conducted a study from 2017 to 2019 in which we collected a novel multi-modal sensor dataset from 20 PwD living in a dementia care unit. Each participant wore an Empatica E4 watch for a maximum period of up to 60 days, leading to a large dataset worth 600 days. This wearable device collects raw acceleration, blood volume pulse, electro-dermal activity, and skin temperature data. The data are annotated with the start and end times of agitation events. Our previous analyses have shown that agitation behavior can be detected with a high area under receiver operating characteristic curve. We are now releasing this novel dataset for the research community to advance research in the field. In this article, we describe the study details, protocol used for annotation, improved agitation labelling, signal processing steps, and feature extraction approach. We present a new baseline on this dataset that can fuel new research in this important area of research.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".