An intelligent video-monitoring system to detect responsive behaviours associated with Alzheimer’s disease and related disorders
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Bibliographic record
Abstract
Responsive behaviours affect 90% of older adults with Alzheimer's disease and related disorders. They have significant consequences including decreased functional independence and quality of life for older adults living in long-term care facilities. An intelligent video-monitoring system (IVS) is being developed to detect responsive behaviours, document their causes and alert health-care professionals to ensure immediate intervention. Purpose To test the IVS's efficacy for responsive behaviour detection. Methods Two occupational therapy students completed a simulation study in an apartment-laboratory under the supervision of experts in gerontology (clinicians, researchers). Four responsive behaviours (aggressiveness, apathy, motor behaviours, vocal behaviours) were realistically replicated across six scenarios, and five were repeated under three different luminosity conditions (total: 16 scenarios). The IVS detects responsive behaviours based on unusual movements or screaming in a specific location. To assess its detection capacity, the scenarios were divided into actions to record true and false positives (TP, FP), and true and false negatives (TN, FN). Sensitivity and specificity were then calculated. The quality of sound, images, and alerts was also analysed. Findings Seventeen TP, two FP, 42 TN, and three FN were recorded, generating an overall sensitivity of 85% and specificity of 95%. Sensitivity and specificity of 100% were obtained for apathy and aggressiveness scenarios. Motor behaviour scenarios achieved a sensitivity of 75% and specificity of 84%, and verbal behaviour scenarios obtained a sensitivity of 50% and specificity of 100%. With this IVS, the quality of the images was satisfactory, but the sound recording was poor. Alerts were received on average 32.4 seconds after detection. Discussion The IVS is an innovative technology that can contribute to responsive behaviour management by immediately detecting such behaviours and helping identify their causes (by recording the previous 30 seconds). These results validate the IVS's potential for responsive behaviour detection before its use is explored in real contexts.
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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 | 0.001 | 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 it