Incremental learning with accuracy prediction of social and individual properties from mobile-phone data

Yaniv Altshuler, Nadav Aharony, Micky Fire, Yuval Elovici, Alex Pentl,

2012 International Conference on Privacy, Security, Risk and Trust and 2012 …, 2012

As truly ubiquitous wearable computers, mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today’s smart phones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals regarding the phone, its user, and their environment. A great deal of research effort in academia and industry is put into mining this data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, and so on. In many cases this analysis work is the result of exploratory forays and trial-and-error. Adding to the challenge, the devices themselves are limited platforms, hence data collection campaign must be carefully designed in order to collect the signals in the appropriate frequency, avoiding the exhausting the the device’s limited battery and …