People Projects Publications Software Data Policy Press Videos FAQ Vintage
Projects Detection and Analysis of Driver Stress
Driving is an ideal test bed for detecting stress in natural situations. Four types of physiological signals (electrocardiogram, electromyogram, respiration, and skin conductivity related to autonomic nervous system activation) were collected in a natural driving situation under various driving conditions. The occurrence of natural stressors was designed into the driving task and validated using driver self-report, real-time, third-party observations, and independently coded video records of road conditions and facial expression. Features reflecting heart-rate variability derived from the adaptive Bayesian spectrum estimation, the rate of skin-conductivity orienting responses, and the spectral characteristics of respiration were extracted from the data. Initial pattern-recognition results show separation for the three types of driving states: rest, city, and highway, and some discrimination within states for cases in which the state precedes or follows a difficult turn-around or toll situation. These results yielded from 89-96 percent accuracy in recognizing stress level. We are currently investigating new, advanced means of modeling the driver data.

Group Members:

[mit][media lab + Room E15-419 + 20 Ames Street + Cambridge, MA 02139]