In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment
URL: http://arxiv.org/abs/2311.09273v1
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Abstract
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.
Summary
Moshfeghi, Jan, Conniff and colleagues developed low-cost in-vehicle sensing hardware to capture high-precision positioning and telematics data from older drivers in naturalistic daily driving, with the goal of detecting early signs of mild cognitive impairment (MCI). Statistical comparisons between drivers with and without MCI showed that MCI drivers exhibited smoother and safer driving patterns, suggesting awareness of their condition and avoidance of erratic maneuvers. Random Forest classifiers identified number of night trips, total number of trips, and education as the most influential features distinguishing the groups. The work demonstrates feasibility of unobtrusive vehicle-based monitoring as an early-warning channel for cognitive decline in older adults.
Key finding
Older drivers with MCI showed smoother, more cautious naturalistic driving than non-MCI peers, and Random Forest models identified night-trip count, total-trip count, and education as the strongest features distinguishing them.
Methodology
Naturalistic in-vehicle telematics study using custom low-cost positioning hardware on older drivers with and without MCI; statistical group comparisons plus Random Forest feature-importance analysis (arXiv:2311.09273).
Quality score: 5 / 5