Electronic Theses and Dissertations

Identifier

1037

Sudip Vhaduri

Date

2014

Document Type

Thesis

Degree Name

Master of Science

Major

Computer Science

Committee Chair

Santosh Kumar

Committee Member

King-Ip Lin

Committee Member

Scott Fleming

Abstract

Driving is known as a daily stressor and measurement of driver's stress in real-time can improve the awareness of stress for drivers, their cars, and their phones. Integrating sensors in future cars can help assess driver's stress, but it requires either wearing sensors by the driver or instrumenting the car. In this thesis, we present "GStress", a model to estimate driver's stress using only Smartphone GPS traces. By obviating any burden on the driver or the car, our approach has a better chance of wider adoption worldwide. 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 more than 10 hours) in their natural environment, including during driving. Each participant had 10 or more driving episodes over the course of the study (for a total of 37 hours of driving data). This being the first work of its kind, provides a correlation of over 0.7 between the actual and estimated driving stress by identifying some major factors such as stops, turns and brakings that contribute to the stress of a driver. Incorporation of other factors in the model as well as use of more advanced modeling approaches can further improve the accuracy of the model.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to the local University of Memphis Electronic Theses & dissertation (ETD) Repository.

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