Workshop day: 12th September, 2016
The Quantified Self (QS) movement, also known as Personal Informatics (PI), has the goal to collect personal data on different aspects of people’s daily lives with technological tools. Recently, we have seen increasing complexity in the Quantified Self domain. First, we have more data being collected, thanks to the availability of an increasing number of apps and wearables for self-tracking. There are also more types of information to be combined, as activity recognition algorithms are now able to recognize a variety of behaviors and activities that can be mashed up and provide multifaceted views on the user. This provides new opportunities for the use of large collections of digital traces, which can go beyond behavior change for exploring new personalized services in education, entertainment, transportation and so on. Despite this growing complexity, the Quantified Self still lacks a discussion on what all these personal data gathered could represent for users, what meaning they may have, and value they may provide
In this new edition of the New Frontiers of Quantified Self workshop, we want to investigate how to go beyond numbers in QS. Our aim is to explore how QS could help people make sense of their own personal information in the future.
To this aim, we are looking for:
i) Novel technologies for gathering data, capable of detecting new aspects of the individual’s life (e.g., cognitive states, social activities, habits)
ii) Novel solutions for mashing-up heterogeneous sources of personal data to provide users with a multifaceted mirror of themselves.
iii) Solutions for mining personal data to find new knowledge (e.g. machine learning techniques, data mining).
iv) Novel ways to engage users in exploring their data and in extracting value from them (e.g., through sense-making, storytelling, gamification).
v) Novel visualizations for easing the sense making of the collected data, going beyond graphs and stats and allowing experiences to emerge from numbers.
vi) Novel applications that exploit the increasing amount of personal data for improving users’ self-knowledge or providing them with new valuable services (e.g., through targeted recommendations, adaptive interfaces).
Relevant workshop topics include but are not limited to: i) Novel technologies for self-tracking
ii) Mash-up platforms
iii) Novel visualizations of personal data
iv) Methodologies and technologies for transforming data into knowledge
v) Novel methods and tools for making sense of data
vi) Novel applications and services enabled by personal data
vii) Thought-provoking insights on how to refocus QS technologies on the individual’s subjectivity
viii) Theoretical reflections on how QS technologies could evolve in the future
ix) Methodologies for evaluating the subjective experience of QS applications
x) Use cases that investigate the effectiveness of novel solutions for QS
We will accept both position papers and research papers, case studies, future research challenges and reflections, up to six pages long, suggesting new ways for making the data collected by QS tools more valuable, interpretable and subjective. Papers will be reviewed by the program committee based on their pertinence with the workshop topics, quality of the exposition and, mainly, potential to trigger discussions and insights for inspiring the design of new solutions during the workshop.Papers should be in pdf format and should not be anonymized.
All the accepted manuscripts will be included in the ACM Digital Library and supplemental proceedings of the main conference. All workshop papers must be up to six pages long in the SIGCHI Extended Abstract format, which can be downloaded here.
The deadline for submission is
June 7 June 14 (extended), 2016.
For any information write to: firstname.lastname@example.org
Amon Rapp, University of Torino
Federica Cena, University of Torino
Judy Kay, University of Sydney
Bob Kummerfeld, University of Sidney
Frank Hopfgartner, University of Glasgow
Till Plumbaum, Technische Universität Berlin
Jakob Eg Larsen, Technical University of Denmark
Daniel A. Epstein, University of Washington
Rúben Gouveia, Madeira Interactive Technologies Institute