Estimating Drivers' Stress from GPS Traces

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.

Signal Processing of Multimodal Mobile Lifelogging Data Towards Detecting Stress in Real-World Driving

“…Measuring stress within drivers usually occurs via simulators [17][18][19] as there is considerable difficulty, effort and risk involved in collecting data in the natural environment [20]. For instance, Katsis et al [17] utilized facial electromyography (fEMGs), electrocardiogram (ECG), respiration and skin conductance within support vector machines (SVMs) and adaptive neuro-fuzzy inference system (AN-FIS) to detect high stress, low stress, disappointment, and euphoria within a simulated car racing environment.…”

“…For the majority of studies who have conducted experiments outside of a laboratory it has been noted that participants often have to follow strict supervision and drive preplanned routes, for a limited time [20]. For instance, Singh et al [21] have utilised Photoplethysmogram (PPG), Galvanic Skin Response (GSR) and respiration data within a Cascade Forward Neural Network (CASFNN).…”

Design Factors of Longitudinal Smartphone-based Health Surveys

“…No push notifications were used to alert users about the availability of surveys and the study showed that overall compliance dropped over the course of the study. In another study, researchers collected various contextual data (such as stress, smoking, drinking, location, transportation mode, and physical activity) using smartphone-based surveys [56]. In addition to the survey data, they also collected various phone sensor data and physiological data (such as heart rate and respiration) from wearables, which can also provide insights into human aspects of data collections.…”

Opportunistic and Context-Aware Affect Sensing on Smartphones

“…Doryab et al 19 collected noise amplitude, location, Wi-Fi SSIDs, light intensity, and movement to determine sleeping and social behavior to detect change in personal behavior of depressed patients. Vhaduri et al 20 using GPS traces found that major driving events (e.g., stops, braking) increase stress of the driver. To determine stress Alexandratos 21 used smart watch and a heart rate monitor coupled with a smartphone to gather skin conductance and heart rate data, respectively.…”