Using Machine Learning to Identify Features from Home Sensors that Predict Care Partner Burden
Home sensors may provide a method to continuously assess daily activities that contribute to the stress level and burden of care partners. Novel ML analysis techniques could help to identify relevant outcome measures from the large amount of data collected by home sensors. Ongoing work is determining the combination of sensors from the technology platform that best predict burden level.
Authors
- Neil Thomas (ORCID: https://orcid.org/0000-0003-4946-831X)
- Zachary Beattie (ORCID: https://orcid.org/0000-0002-4844-7122)
- Rajib Dey (ORCID: https://orcid.org/0000-0002-4701-3939)
- Lisa Sheehy (ORCID: https://orcid.org/0000-0002-5844-0952)
- Laura Ault (ORCID: https://orcid.org/0000-0001-9871-8368)
- Bahareh Chimehi
- Joel S. Steele (ORCID: https://orcid.org/0009-0008-4127-5664)
- Frank Knoefel (ORCID: https://orcid.org/0000-0002-5760-4484)
- Lyndsey M. Miller
- Julien Larivière‐Chartier
- Jeffrey A Kaye
- Bruce Wallace
Institutions
- University of Ottawa (CA)
- Oregon Health & Science University (US)
- University of North Dakota (US)
- Oregon Center For Applied Science (United States) (US)
- Alzheimer’s Disease Neuroimaging Initiative (US)
- Institute on Aging (US)
- VA Portland Health Care System (US)
- Bruyère (CA)
- Carleton University (CA)
Publication Details
- Journal
- Alzheimer s & Dementia
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/alz70858_107291
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00