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Visualizing Quantified Self Data Using Avatars

Authors:
Isabella Nake
Aris Alissandrakis
Janosch Zbick

Keywords: Quantified Self; Avatars; Data Visualization

Abstract:
In recent years, it is becoming more common for people to use applications or devices that keep track of their activities, such as fitness activities, places they visited, the music they listen to, and pictures they took. These data are used by the services for various purposes, but usually there are limitations for the users to explore or interact with them. Our project investigates a new approach of visualizing such Quantified Self data, in a meaningful and enjoyable way that gives the users insights into their data. The paper discusses the feasibility of creating a service that allows users to connect the activity tracking applications they already use, analyse the amount of activities, and then presents them the resulting information. The visualization of the information is proposed as an avatar that maps the different activities the user is engaged with, along with the activity levels, as graphical features. Within the scope of this work, several user studies were conducted and a system prototype was implemented to explore how to build, using web technologies, such a system that aggregates and analyses personal activity data, and also to determine what kind of data should and can be collected, to provide meaningful information to the users. Furthermore, it was investigated how a possible design for the avatar could look like, to be clearly understood by the users.

Pages: 57 to 66

Copyright: Copyright (c) IARIA, 2016

Publication date: April 24, 2016

Published in: conference

ISSN: 2308-4138

ISBN: 978-1-61208-468-8

Location: Venice, Italy

Dates: from April 24, 2016 to April 28, 2016