Identifying transportation mode based on smartphone sensor data using machine learning tools and statistical methods

Detecting an individuals transportation mode has an invaluable role in applications, by allowing the application to be aware of users current context, and modify their functionality accordingly. There has been numerous research in this area, each using a different approach and achieving different outcomes. The goal of this internship to better understand the state of the art technology in classifying modes of transportation (e.g. walking, biking, driving) using the data from smartphone sensors such as GPS or accelerometer, and subsequently leverage this to provide a better behavioural analysis platform for academic research, particularly in public-health and social science domain.

Faculty Supervisor:

Michael Horsch

Student:

Aydin Teyhouee

Partner:

Ethica Data Services Inc

Discipline:

Computer science

Sector:

Information and communications technologies

University:

Program: