TRI-Developing a personalized Guardian system to assist aging drivers through machine learning, sensor fusion and data mining
Investigators: Yi Lu Murphey, Yung-Wen Liu, Lisa Jeanne Molnar, Bruno J. Giordani, David W. Eby, Carol Catherine Persad, Umesh C. Patel
Funding: Toyota Research Institute, 2017-2018 (Task Order 1 to Master Collaborative Research Agreement)
This project investigates innovative technologies that can be used for building a Guardian system to assist aging drivers through the use of machine learning, sensor fusion, and data mining. Driving is a complex operation that involves primary and often secondary cognitive and motor tasks. The rapid increase in the older adult population worldwide, many of whom will continue to drive regardless of cognitive impairment and dementia, will require new approaches to help this population maintain driving safety, and novel ways to monitor and measure ongoing health status.
This project will develop enabling technologies to support long-term, real time, in-vehicle monitoring, learning, and assessment of older adults' driving behavior and physiological signatures (characterized by heart rates, respiration, and skin conductance) under a set of well-defined driver workload-related driving scenarios.
We focus on studying healthy older drivers as well as drivers diagnosed with Mild Cognitive Impairment (MCI). Research, including our own, shows that 15-20% of people age 65 or older have measureable declines in cognitive function, and that people with MCI have poorer driving abilities related to level of cognitive impairment. Research also shows that about 30-40% of people with MCI will develop dementia within 5 years. It has been estimated that up to one-third of older adults with dementia continue to drive and it is very likely they are also still driving as frequently as other drivers.