Combining Video‐Based Skeletal and Wearable Physiological Data for Early Detection of Agitation in Dementia Using Deep Learning
Integrating wearable physiological data with video-derived skeletal keypoints enhances the early detection of agitation in dementia care units. This approach is feasible, privacy-compliant, and improves predictive accuracy. Further research is underway to refine and scale this model for broader clinical application.
Authors
- Abeer Badawi (ORCID: https://orcid.org/0000-0002-0107-9457)
- Amer M. Burhan (ORCID: https://orcid.org/0000-0002-9888-008X)
- Sara Elgazzar
- Somayya Elmoghazy
- Khalid Elgazzar
Institutions
- University of Toronto (CA)
- Ontario Shores Centre for Mental Health Sciences (CA)
- Ontario Tech University (CA)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70857_105825
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00