Characterizing User Mobility Using Mobile Sensing Systems

Recent technological advances and the ever-greater developments in sensing and computing continue to provide new ways of understanding our daily mobility. Smart devices such as smartphones or smartwatches can, for instance, provide an enhanced user experience based on different sets of built-in sensors that follow every user action and identify its environment. Monitoring solutions such as these, which are becoming more and more common, allows us to assess human behavior and movement at different levels. In this article, extended from previous work, we focus on the concept of human mobility and explore how we can exploit a dataset collected opportunistically from multiple participants. In particular, we study how the different sensor groups present in most commercial smart devices can be used to deliver mobility information and patterns. In addition to traditional motion sensors that are obviously important in this field, we are also exploring data from physiological and environmental sensors, including new ways of displaying, understanding, and analyzing data. Furthermore, we detail the need to use methods that respect the privacy of users and investigate the possibilities offered by network traces, including Wi-Fi and Bluetooth communication technologies. We finally offer a mobility assistant that can represent different user characteristics anonymously, based on a combination of Wi-Fi, activity data, and graph theory.