Independent Living


To test user-installed technology that monitors older adults at home and provides reminders to perform key tasks

Anticipated Impact: 

Cost savings for rehabilitation and nursing care services, and improved quality of life for the elderly


Most elderly and mildly cognitively impaired individuals want to stay in familiar home settings (which has better health outcomes and saves considerable money) as long as possible. However, performing Activities of Daily Living (ADLs) such as bathing and taking medications can be difficult for these individuals to remember. Using innovative, software-based machine learning algorithms and wireless sensors, developed by the principal investigator and her team partly through a 2008 LSDF grant, this study will deploy a “smart home in a box.” This consists of an unintrusive, inexpensive, user-installed system that “learns” what the house’s occupant(s) do and reminds them to maintain ADLs. The study will test the feasibility of ordinary people (caregivers, generally) installing and maintaining the system. Prompting of residents who have forgotten to perform a given ADL will be tested in a subset of homes. With LSDF funding, Washington State University has engaged an entrepreneur in residence, Krishnan Gopalan, to explore commercialization of the technology, either through licensing to an existing company or by creating a new company for this purpose.

Collaborating organization: Carabiner Consulting

See also:

Independent Living

Grant Update

Principal Investigator:
Diane Cook
Grantee Organization:
Washington State University
Grant Title:
CASAS-Care: Pilot Study of a Smart Home in a Box
Grant Cohort and Year:
2011 First Round Commercialization (03)
Grant Period:
01/16/2012 - 01/15/2014 (Completed)
Grant Amount:
The main outcomes of this grant to date are 1) the development and evaluation of technologies for automated activity-aware prompting reminders and 2) the identification of unique aspects of this project that differentiate it from other commercial systems (the machine learning software and the modular middleware design) and the identification of possible commercial paths for the product.

Impact in Washington

Location of LSDF Grantee
Locations of Collaborations/Areas of Impact

Legislative Districts:
9, 11, 34, 36, 37, 43, 46

Health Impacts

Independent Living