Detecting stress during real-world driving tasks using physiological sensors

This paper presents methods for collecting and analyzing physiological data during real-world driving tasks to determine a driver's relative stress level. Electrocardiogram, electromyogram, skin conductance, and respiration were recorded continuously while drivers followed a set route through open roads in the greater Boston area. Data from 24 drives of at least 50-min duration were collected for analysis. The data were analyzed in two ways. Analysis I used features from 5-min intervals of data during the rest, highway, and city driving conditions to distinguish three levels of driver stress with an accuracy of over 97% across multiple drivers and driving days. Analysis II compared continuous features, calculated at 1-s intervals throughout the entire drive, with a metric of observable stressors created by independent coders from videotapes. The results show that for most drivers studied, skin conductivity and heart rate metrics are most closely correlated with driver stress level. These findings indicate that physiological signals can provide a metric of driver stress in future cars capable of physiological monitoring. Such a metric could be used to help manage noncritical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers.

I. Introduction

The increasing use of on-board electronics and in-vehicle information systems has made the evaluation of driver task demand an area of increasing importance to both government and industry [1], and understanding driver frustration has been listed by international research groups as one of the key areas for improving intelligent transportation systems [2]. Protocols to measure driver workload have been developed using eye glance and on-road metrics, but these have been criticized as very costly and difficult to obtain [3], and uniform heuristics, such as the 15-s rule for total task time, designed to provide an upper limit for the total time allowed for completing a navigation system task, do not provide flexibility to account for changes in the driver's environment [3]. As an alternative, this study shows how physiological sensors can be used to obtain electronic signals that can be processed automatically by an on-board computer to give dynamic indications of a driver's internal state under natural driving conditions. Such metrics have been proposed for fighter pilots [4] and have been used in simulations [5], but they have not been tested on stress levels approximating a normal daily commute using sensors that do not obstruct drivers' perception of the road.