Related papers: In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment

In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment

URL: http://arxiv.org/abs/2311.09273v1
Date: Wed, 15 Nov 2023 15:47:24 GMT
Title: In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment
Authors: Sonia Moshfeghi, Muhammad Tanveer Jan, Joshua Conniff, Seyedeh Gol Ara Ghoreishi, Jinwoo Jang, Borko Furht, Kwangsoo Yang, Monica Rosselli, David Newman, Ruth Tappen, Dana Smith
Abstract summary: The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data. Our statistical analysis comparing drivers with mild cognitive impairment (MCI) to those without reveals that those with MCI exhibit smoother and safer driving patterns. Our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.
Score: 0.8426358786287627
License: http://creativecommons.org/licenses/by/4.0/
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.

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