Visualization of Quantified Self data from Spotify using avatars

Abstract: The increased interest for self-tracking through the use of technology has given birth to the Quantified Self movement. The movement empowers users to gain self-knowledge from their own data. The overall idea is fairly recent and as such it provides a vast space for exploration and research. This project contributes to the Quantified self movement by proposing a concept for visualization of personal data using an avatar. The overall work finds inspiration in Chernoff faces visualization and it uses parts of the presentation method within the project design.   This thesis presents a visualization approach for Quantified Self data using avatars. It tests the proposed concept through a user study with two iterations. The manuscript holds a detailed overview of the designing process, questionnaire for the data mapping, implementation of the avatars, two user studies and the analysis of the results. The avatars are evaluated using Spotify data. The implementation offers a visualization library that can be reused outside of the scope of this thesis. The project managed to deliver an avatar that presents personal data through the use of facial expressions. The results show that the users can understand the proposed mapping of data. Some of the users were not able to gain meaningful insights from the overall use of the avatar, but the study gives directions for further improvements of the concept.