Conference Paper2014Cited by 50

Estimating drivers' stress from GPS traces

Sudip Vhaduri, Amin Ali, Moushumi Sharmin, Karen Hovsepian, Santosh Kumar

Proceedings of the 6th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

ACM, pp. 1–8

  • Good Health and Well-being — Relevance: 83%
DOI: 10.1145/2667317.2667335Open AccessURL

Abstract

Driving is known to be a daily stressor. Measurement of driver's stress in real-time can enable better stress management by increasing self-awareness. Recent advances in sensing technology has made it feasible to continuously assess driver's stress in real-time, but it requires equipping the driver with these sensors and/or instrumenting the car. In this paper, we present "GStress", a model to estimate driver's stress using only smartphone GPS traces. The GStress model is developed and evaluated from data collected in a mobile health user study where 10 participants wore physiological sensors for 7 days ( for an average of 10.45 hours/day) in their natural environment. Each participant engaged in 10 or more driving episodes, resulting in a total of 37 hours of driving data. We find that major driving events such as stops, turns, and braking increase stress of the driver. We quantify their impact on stress and thus construct our GStress model by training a Generalized Linear Mixed Model (GLMM) on our data. We evaluate the applicability of GStress in predicting stress from GPS traces, and obtain a correlation of 0.72. By obviating any burden on the driver or the car, we believe, GStress can make driver's stress assessment ubiquitous.

Keywords

  • GPS tracks
  • mobile health
  • generalized linear mixed models
  • smartphone sensing
  • real-time stress monitoring
  • physiological sensors
  • driver behavior
  • braking

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