Human-Centered Multi-Sensor Framework for Identifying Driving Patterns Associated with Cognitive Decline Through...

Driving requires complex cognitive abilities, making it a promising behavioral domain for identifying Mild Cognitive Impairment (MCI). This paper presents a pilot proof-of-concept framework deploying AutoPi telematics units across 51 older adult drivers (10 MCI, 41 cognitively unimpaired) over a 28-month observation window, yielding 20,145 trips across GPS, IMU, and OBD-II sensor streams. A multi-stage analytical pipeline, K-Means clustering for behavioral profiling, Random Forest feature ranking, Welch's t -tests with Benjamini-Hochberg correction, and L1-regularized logistic regression with participant-level leave-one-out cross-validation, achieves an AUC of 0.698 (95% CI: 0.493-0.872) with a sensitivity of 0.800. Throttle position variability and mean throttle application are the strongest sensor-derived predictors (Cohen's d = 0.86 each), reflecting impaired speed regulation consistent with executive dysfunction in MCI; however, the cohort's gender imbalance (9 of 10 MCI participants are female) means that demographic factors, particularly gender, contribute substantially to overall model discrimination. A sensitivity analysis excluding gender reduces AUC to 0.598, comparable to the telematics-only result (0.595), confirming that the driving-behavior signal is meaningful but modest when demographic confounding is removed. Cold-start analysis indicates that approximately 50 trips, roughly four months of naturalistic driving, constitutes the minimum viable observation window for reliable screening. Subgroup analyses reveal performance disparities attributable to cohort composition rather than systematic model bias. Findings support telematics-based MCI monitoring as a promising framework warranting validation in larger, gender-balanced cohorts before clinical deployment.