Smart Home Technologies
When: Monday, October 10, 2011 - 4:00pm - 4:50pm
Where: KEC 1001
Speaker Information
Speaker Name: Dr. Aaron S. Crandall
Speaker Title/Description:
Postdoctoral Research Associate
Washington State University
Center for Advanced Studies in Adaptive Systems
Speaker Biography:
Dr. Crandall is a postdoctoral research associate at Washington State University's Center for Advanced Studies in Adaptive Systems (CASAS). His work has included the application of behaviometrics to smart home systems, as well as techniques for handling multiple residents within smart homes. These tools are geared towards the overall goals of the CASAS smart home research group to build novel approaches to support aging in place and eldercare technologies. Dr. Crandall received his Ph.D. in Computer Science from Washington State University in 2011, his Master of Computer Science from Oregon Health and Science University in 2006 and his Bachelor of Electrical Engineering from the University of Portland in 2001.
The population of the United States is aging. By 2040 the largest age group
will be 80-plus. We do not have the care facilities or care providers to
handle this upcoming wave of older adults. New approaches and tools are needed
to address this issue. There is a movement among the gerontology field to move
towards an aging in place philosophy, where people need to live in their homes
longer instead of moving to a care facility. The CASAS group at WSU has been
focused on building smart home technologies that can assist residents and care
givers with this aging in place philosophy.
This talk on smart home technologies will introduce the CASAS research
environment and the wide range of applications that these smart homes are used
for. The CASAS group has constructed a series of smart home testbeds in
private homes for real world data collection. This has enabled them to explore
algorithms for interpreting and mining smart home data using a wide range of
artificial intelligence, machine learning and data mining strategies. The goal
is to enable computers to build accurate models of what and how well the smart
home residents are doing in their day to day activities. With these new
capabilities, systems to help residents live in their homes longer become more
feasible.
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