Learning human contexts through unobtrusive methods.

摘要

Learning human contexts is critical to the development of many applications, ranging from healthcare, business, to social sciences. Most existing work, however, acquires contextual information in an obtrusive manner -- they may require subjects to carry mobile devices, or rely on self or peer report to report data. In this dissertation, we present two unobtrusive techniques that can help us learn important human contextual information including count, location, trajectory, and speech characteristics. We first present SCPL, a radio frequency-based device-free localization technique. SCPL is able to count how many people are in an indoor setting and track their locations by observing how they disturb the wireless radio links in the environment. Second, we present Crowd++, a smartphone-based speech sensing technique, which records a conversation and automatically counts the number of people in the conversation without prior knowledge of their speech characteristics. Both techniques are unobtrusive, low-cost, and private, which can thus enable a large array of important applications that rely upon the knowledge of human contextual information.