Individualized stress detection using an unmodified car steering wheel

Abstract

In-car passive stress sensing could enable the monitoring of stress biomarkers while driving and reach millions of commuters daily (i.e., 123 million daily commuters in the US alone). Here, we present a nonintrusive method to detect stress solely from steering angle data of a regular car. The method uses inverse filtering to convert angular movement data into a biomechanical Mass Spring Damper model of the arm and extracts its damped natural frequency as an approximation of muscle stiffness, which in turn reflects stress. We ran a within-subject study (N = 22), in which commuters drove a vehicle around a closed circuit in both stress and calm conditions. As hypothesized, cohort analysis revealed a significantly higher damped natural frequency for the stress condition (P = .023, d = 0.723). Subsequent automation of the method achieved rapid (i.e., within 8 turns) stress detection in the individual with a detection accuracy of 77%.