frontiersQS 2015 - 2015 workshop. New frontiers of Quantified Self: finding new ways for engaging users in collecting and using personal data

Quantified Self (QS), also known as Personal Informatics (PI), is a school of thought that aims to use technology for acquiring and collecting personal data on different aspects of the daily lives of people. These data can be internal states (e.g. mood or glucose level in the blood), or indicators of performance (e.g. the kilometers run), or behaviors and work habits (e.g. sleep or level of distraction). The purpose of collecting these data is the gaining of self-awareness and self-knowledge or some kind of change or improvement (behavioral, psychological, etc.).
In spite of the fast growth in the market of these kinds of tools, many issues arise when we consider their usage in the daily lives of common people, such as: i) Motivation in tracking data and accuracy of the data tracked; ii) Capability of managing and integrating different kinds of personal data; iii) Understandability of the gathered data; iv) Meaningfulness of the visualizations provided and the ability to make data actionable; v) Motivations for long-term usage.
In this workshop, we want to tackle some of these issues, going beyond the Quantified Self to explore both new technologies and design techniques that could be applied to this field. We aim to find new ways for tracking, managing, interpreting and visualizing personal data, imagining how the Quantified Self technologies will evolve in the next years and what we could do to make them closer to the users’ needs and desires.
To this aim, we are looking for:
New modalities for engaging users in self-reporting their data through new approaches such as tangible interfaces, gamification, smart objects, etc.
New solutions for integrating heterogeneous kinds of personal data to provide users with a more comprehensive picture of their “selves”: through e.g. machine learning techniques, mash-up systems, etc.
New tools for simplifying the management and interpretation of the gathered data to provide thought provoking insights on users’ current behaviors and useful suggestions for improving their habits: through e.g. user modeling techniques, data mining, etc.
New meaningful visualizations of human behavior data to turn data into affordances for action by taking inspiration e.g. from the entertainment world (narratives, video games, etc.), social data visualization, or leveraging new interactions techniques, such as natural interfaces.