Location-Based Activity Recognition
Knowledge of a person’s location provides important context information for many applications, ranging from services such as E911 to personal guidance systems that help cognitively-impaired individuals move safely through their community. Location information is also extremely helpful for estimating a person’s high-level activities. In this talk we show how Bayesian filtering and conditional random fields can be applied to estimate the location and activity of a person using sensors such as GPS or WiFi. The techniques track a person on graph structures that represent a street map or a skeleton of the free space in a building. We also show how to learn a user’s significant places and daily movements through the community. Our models use multiple levels of abstraction so as to bridge the gap between raw GPS measurements and high level information such as a user’s mode of transportation, her current goal, and her significant places (e.g. home or work place). Finally, we will discuss recent work on using a multi-sensor board so as to better estimate a person’s activities.