A data physicalization framework for the Quantified Self. One output of my thesis at the end of my MEng program at the University of Bristol. The framework included three components operating within the same ecosystem and APIs. The framework aggregated data from Moves, Instagram, last.fm, Fitbit, in order to create a data journal that also acted as a provocation against the increasing abstraction and distancing of human-produced data from its creators.



The quantification of lifestyles has become possible in the past few years through advances in wearable computing and mobile phone hardware. As a result, the ecosystem for self-tracking applications and specialized devices has proliferated. Devices and applications such as Fitbit, Nike+, Sensoria, Strava, and more have found widespread appeal in capturing activity-related data, and each one offers individual dashboards for its visualization. This newfound wealth in the availability of data, however, is not accompanied by a holistic understanding of the data points produced. In other words, this deeply personal information is not presented under a context in which its importance may be understood and its meaning inherently interpreted.

The limitations in the current state of lifestyle quantification therefore fall into two groups. On one hand, the pool of available data is not adequately leveraged due to the absence of a central system responsible for the unification of data from various sources and services. On the other hand, the personal data that is visualized is not presented in a manner proportionally beneficial to its importance, in regards to encouraging users to reflect on or alter their lifestyles. The goal of this project is to present a proof-of-concept solution and provide structural blueprints for a system, the conceptual and technical underpinnings of which aim to address the above two limitations.

The framework presented will consist of appropriate back-end infrastructure enabling users to authorize, connect, and merge various data warehouses of self-tracking data. The presentation of such data will be realized by connected actuated hardware displays and tangible interfaces, the form and function of which will be driven by custom parameters from users’ personal information.

Two such hardware prototypes will be presented: The Augmented Reflection Mirror is a custom interface that distorts a user’s reflection based on a custom parameter of their recent lifestyle, while a connected thermal printer presents customized data summaries of user activity. Among the presented outputs is also a web dashboard which visualizes aggregated lifestyle data. The underlying focus for the framework throughout its development was to ensure scalability, extensibility, and ease-of-use for potential users. In the pages that follow, the conceptual groundings of the system are laid out, along with its design rationale and development process.