Project: Visualizing quantified self data using avatars

The purpose of the project is to explore creative ways to visualize Quantified Self data, aggregated from social media and Internet of Things services that a person might be using.

Project information

Project manager
Aris Alissandrakis
Media Technology (Department of Media Technology, Faculty of Technology)

More about the project

Large amounts of data are generated by and collected (usually by the different service providers) from everyday activities of users, as well as from their use of their smart devices, wearables and, of course, mobile phones. The difficulty lies, as usually noted in Big Data domain discussions, in interpreting such data into useful information.

Numerous applications already exist which can visualize the data, but further research is necessary to offer easily-understandable information visualization, especially for cross-referencing different types of data.
Although used by the applications and services for a variety of purposes, these personal Big Data remain mostly unavailable to the users, with limited ways for them to interact and explore.

skiss över konceptet

In the Visualizing quantified self data using avatars project, we attempt to address this. We want to explore ways in which the ownership of these generated data is reclaimed for more personal uses, such as obtaining easily accessible insights based of the users' own information.

The project intends to explore a new approach for visualizing Quantified Self data that are collected and aggregated from a variety of social media services or wearable devices that a person might be using, and represented as a (constantly updated) graphical avatar. The amount of activity is something which the users may not be consciously aware of, or it may not directly correspond to their personal impression. In that respect, this visualization could also contribute to their awareness of the extent that the different services collect personal data from them.

Initial work has produced so far a master thesis (by Isabella Nake) and two conference papers, one of which has also received a best paper award at the ACHI 2016 conference.