In Partial Fulfillment of the Requirements for the Degree of
Doctor of Philosophy
Will defend his PhD dissertation proposal
The crux of this dissertation is to understand human walking behavior. Walking is one of the most fundamental human activities. Study of walking behavior is difficult because it demands ubiquitous monitoring of a large subject pool for an extended period of time and consideration of multiple covariates. This research examines three factors that may significantly affect walking activity. The first factor is self-awareness. To this end, we use a smartphone app (iBurnCalorie) developed in a prior PhD dissertation, which transforms accelerometer readings to caloric values. The app keeps the user informed about her/his walking output. The original caloric transformation, assumes that the users walk on a flat ground, which is not always true. In our research we propose a novel calibration method that takes into account surface inclination, thus, improving the accuracy of the initial mapping.
The second factor that likely affects walking behavior is the weather. For people living in big cities, man-made weather is of primary concern. The most common pollutant is ozone and walking under unsafe ozone levels is unhealthy. The current ozone alert technologies are neither highly dynamic nor highly specific in terms of locale and hence, not very informative for walkers. In this dissertation, we have designed, developed, and evaluated an app called OzoneMap that delivers spatio-temporal ozone information. In a study that we performed, we documented the relevance and usefulness of such ozone information for walkers. Next, we plan on incorporating the OzoneMap app to the iBurnCalorie app.
The third factor that likely affects walking behavior is the role models that people compare themselves against. In fact, the iBurnCalorie app supports the search and selection of role models from its user base; the users can monitor themselves and somebody else (role model) they freely choose. We plan on analyzing the entrainment effects among dominant and non-dominant nodes in the app's walker network. Identifying positive and negative reinforcement patterns that occur naturally, will inform future interventions, where struggling walkers based on their characteristics will be optimally matched to role models.
The outcome of this work will be a new iBurnCalorie app that will compute caloric expenditure from walking more accurately, maximize the opportunities for safe walking in polluted metropolitan centers, and virtually pair walkers with role models that stand the best likelihood of behavioral modification.
Date: Tuesday, March 18, 2014
Time: 8:00 AM
Place: Conference Room 302, Health&Biomedical Sciences Center (HSBC)
Faculty, students, and the general public are invited.
Advisor: Prof. Ioannis Pavlidis