Building health persona from personal data streams

Most people already use phones with myriad sensors that continuously generate data streams related to most aspects of their life. By detecting events in basic data streams and correlating and reasoning among them, it is possible to create a chronicle of personal life. We call it Personicle and use this to build individual Health Persona. Such Health Persona may then be used for understanding societal health as well as making decisions in emerging Social Life Networks. In this paper, we present a framework that collects, manages, and correlates personal data from heterogeneous data sources and detects events happening at personal level to build health persona. We use several data streams such as motion tracking, location tracking, activity level, and personal calendar data. We illustrate how two recognition algorithms based on Formal Concept Analysis and Decision Trees can be applied to Life Event detection problem. Also, we demonstrate the applicability of this framework on simulated data from Moves app, GPS, Nike fuel band, and Google calendar. We expect to soon have results for several individuals using real data streams from disparate wearable and smart phone sensors.