Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science

Abstract

The last several years have seen an explosion of interest in wearable computing, personal tracking devices, and the so-called quantified self (QS) movement. Quantified self involves ordinary people recording and analyzing numerous aspects of their lives to understand and improve themselves. This is now a mainstream phenomenon, attracting a great deal of attention, participation, and funding. As more people are attracted to the movement, companies are offering various new platforms (hardware and software) that allow ever more aspects of daily life to be tracked. Nearly every aspect of the QS ecosystem is advancing rapidly, except for analytic capabilities, which remain surprisingly primitive. With increasing numbers of qualified self participants collecting ever greater amounts and types of data, many people literally have more data than they know what to do with. This article reviews the opportunities and challenges posed by the QS movement. Data science provides well-tested techniques for knowledge discovery. But making these useful for the QS domain poses unique challenges that derive from the characteristics of the data collected as well as the specific types of actionable insights that people want from the data. Using a small sample of QS time series data containing information about personal health we provide a formulation of the QS problem that connects data to the decisions of interest to the user.

Keywords: collection of health data; data science; internet of things; knowledge discovery; mobile health; quantified self; wearable devices.