  Driving is a complex daily activity indicating age and disease related
cognitive declines. Therefore, deficits in driving performance compared with
ones without mild cognitive impairment (MCI) can reflect changes in cognitive
functioning. There is increasing evidence that unobtrusive monitoring of older
adults driving performance in a daily-life setting may allow us to detect
subtle early changes in cognition. The objectives of this paper include
designing low-cost in-vehicle sensing hardware capable of obtaining
high-precision positioning and telematics data, identifying important
indicators for early changes in cognition, and detecting early-warning signs of
cognitive impairment in a truly normal, day-to-day driving condition with
machine learning approaches. Our statistical analysis comparing drivers with
MCI to those without reveals that those with MCI exhibit smoother and safer
driving patterns. This suggests that drivers with MCI are cognizant of their
condition and tend to avoid erratic driving behaviors. Furthermore, our Random
Forest models identified the number of night trips, number of trips, and
education as the most influential factors in our data evaluation.
