The CASAS smart home project designs intelligent approaches
to data collection, data analysis, and decision making
to make environments safer, smarter, and more productive.
We develop innovative, evidence-based and cost-conscious smart
health solutions by identifying unmet needs, developing
technologies, validating them in clinical settings and refining
and optimizing them with the input from patients and clinicians.
The transfer of learned knowledge from source to target helps agents and humans
to collaborate in smart environments.
Using machine learning techniques, we can develop time-bounded and anytime
prediction algorithms based on the needs of the application.
Machine learning applied to brain MRI discovers discriminating
neural regions that may serve as markers for early diagnosis of
cognitive disorders. More...
Representing movement within a smart environment as a
transition graph can improve activity recognition.