Empirical Thresholding on Spatio-Temporal Autoencoders Trained on Surveillance Videos in a Dementia Care Unit
Bibliographic record
Abstract
Agitation is a key behavioural and psychological symptom exhibited by people with dementia. These behaviours can put the patient with dementia and others' health and safety at risk. Surveillance cameras installed in long-term care facilities provide an opportunity to monitor patients continuously and flag behaviours of risks, including agitation. However, agitation behaviours occur rarely and diversely, leading to very small training data. Therefore, an anomaly detection approach is more suitable for this problem. In this paper, we train three baseline spatio-temporal convolutional autoencoders (on raw video, skeletons and segmentation mask) on 21 hours of normal activities and tested it on 9 hours of labelled normal and agitation data collected from a real patient in a dementia unit. The deployment of anomaly detection-based classifiers is challenging in real-world due to the absence of a validation set to obtain an operating threshold to regulate true positive and false positive rates. We present a new approach to create a proxy validation set for unseen agitation events utilizing the outliers within normal activities, and trained two separate autoencoders on normal and outliers activities. Then, we present 11 empirical thresholding approaches (existing, adapted and new) using either only normal training data or the proxy validation set. Our results showed consistently across raw video, skeletons and segmentation masks input that incorporating a proxy validation set improved performance both in terms of geometric means and Matthew's correlation coefficient. This paper highlights the real-world deployment challenges and assessment of the limit of true positives or false positives that can be acceptable in a clinical care environment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".