Campuses:

Activity detection and tracking using smartphone sensor data

Wednesday, December 31, 1969 - 6:00pm
Faculty Advisor: Vittorio Addona, Department of Mathematics, Statistics and Computer Science, Macalester College

Problem Poser: Julian Wolfson, Department of Biostatistics, University of Minnesota


What did you do today? This simple question interests researchers in fields as diverse as psychology, sociology, urban planning, and health care. Traditionally, individuals participating in research studies recall and report this information via a (typically paper-based) trip/activity diary. These diaries are burdensome, prone to recall bias, and limit the quantity and quality of collected data. The widespread adoption of smartphone technology provides an exciting opportunity to improve the way we collect activity information. Specifically, a smartphone equipped with a GPS receiver and an accelerometer can record location and movements without any user input. In this project, students will participate in the development and implementation of mathematical, statistical and computational techniques to automatically detect, identify, and summarize attributes of daily activity and travel episodes using smartphone sensor data.

Through a research grant funded by the US Department of Transportation, Professor Wolfson’s research team is currently developing the SmarTrAc app, which uses these data sources to compile and summarize an individual's daily trips and activities. SmarTrAc will free study participants from the drudgery of cataloging their activities via a diary. This enables researchers to ask more detailed information about choices and motivations. For example, if the application detects that a particular individual usually drives to work but sometimes takes the bus, then it would prompt the user to identify which factors determine their choice of mode of transportation.

Student participants will analyze GPS and accelerometer-derived time series data collected during pilot testing of SmarTrAC. They will tackle questions such as: 1) How does one automatically extract a set of distinct activities/trips from unlabeled time series data? 2) Is it possible to distinguish between multiple modes of transportation on the basis of GPS and/or accelerometer data? 3) Can smartphone sensor data be used to automatically infer the purpose of particular trips and activities?

Required background: Basic statistics, familiarity with statistical or numerical software such as R or MATLAB.

Useful Background: familiarity with Java (language of implementation of SmarTrAC), experience in one (or more) of regression modeling, machine learning, time series data, or mobile device programming