Quantified Self: Analyzing the Big Data of our Daily Life

Abstract

Applications for self tracking that collect, analyze, or publish personal and medical data are getting more popular. This includes either a broad variety of medical and healthcare apps in the fields of telemedicine, remote care, treatment, or interaction with patients, and a huge increasing number of self tracking apps that aims to acquire data form from people’s daily life. The Quantified Self movement goes far beyond collecting or generating medical data. It aims in gathering data of all kinds of activities, habits, or relations that could help to understand and improve one’s behavior, health, or well-being. Both, health apps as well as Quantified Self apps use either just the smartphone as data source (e.g., questionnaires, manual data input, smartphone sensors) or external devices and sensors such as ‘classical’ medical devices (e.g,. blood pressure meters) or wearable devices (e.g., wristbands or eye glasses). The data can be used to get insights into the medical condition or one’s personal life and behavior. This talk will provide an overview of the various data sources and data formats that are relevant for self tracking as well as strategies and examples for analyzing that data with Python.