Detecting Stress During Real-World Driving Tasks Using Physiological Sensors

Jennifer Healey, Rosalind W. Picard


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 twentyfour drives of at least fifty minute duration were collected for analysis. The data were analysed in two ways. Analysis I used features from five minute 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 one second intervals throughout the entire drive, with a metric of observable stressors created by independent coders from video tapes. 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 non-critical in-vehicle information systems and could also provide a continuous measure of how different road and traffic conditions affect drivers. Keywords driver, stress, traffic, automobile, physiology, sensor, signal, affect, recognition, classification, correlate, computer, skin conductance, electromyogram, electrocardiogram, respiration

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