Developing a personalized Guardian system to assist aging drivers through machine learning, sensor fusion and data mining
Project Abstract/Statement of Work:
We propose to investigate 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 driving cognitive and motor tasks. The rapid increase in the older adult population worldwide, many of whom will continue to drive even 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, especially while in remote areas. We propose to 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.
In this pilot study, we will focus on studying healthy older drivers and drivers diagnosed with Mild Cognitive Impairment (MCI). Research, including our own, show that 15-20 percent of people age 65 or older have measureable declines in cognitive function that are noticeable to the person and others, and people with MCI have poorer driving abilities that are generally related to the level of cognitive impairment. Research also shows that about 30-40 percent 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.