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https://ceur-ws.org/Vol-1618/FuturePD_preface.pdf
FuturePD. The future of personal data: envisioning new
personalized services enabled by Quantified Self
technologies
Amon Rapp1, Federica Cena1, Judy Kay2, Bob Kummerfeld2, Frank Hopfgartner3, Jakob Eg
Larsen4, Elise van den Hoven5
University of Torino1, University of Sydney2, University of Glasgow3, Technical University of
Denmark4, University of Technology Sydney, Australia and Eindhoven University of Technology, the
Netherlands5
amon.rapp@gmail.com; cena@di.unito.it; judy.kay@sydney.edu.au; bob.kummerfeld@sydney.edu.au;
frank.hopfgartner@glasgow.ac.uk; Elise.VandenHoven@uts.edu.au
ABSTRACT 2. BACKGROUND AND MOTIVATION
Quantified Self is rising new challenges for user modeling and The knowledge about one’s self, which QS systems can provide,
personalization. In this workshop we aim at exploring the future can be employed in a variety of domains, potentially improving
of personalized services enabled by Quantified Self technologies. people’s everyday life. By enabling people to reflect about
themselves, for example, QS tools can trigger processes of
CCS Concepts behavior change, as the act of self-monitoring often changes
• Human-centered computing➝Human computer interaction. behavior due to its reactive effects [12]. Moreover, self-awareness
can be an effective means to help people move from a stage in
Keywords which they have no intention of modifying their own behavior to
Personal informatics; Quantified Self; Personalization. one in which they decide that they want to take action towards
achieving behavior change [13].
HCI researchers have designed a variety of systems for fostering a
1. INTRODUCTION change in behavior by leveraging personal data (e.g. [9]).
Quantified Self (QS), which is also known as Personal Informatics However, the availability of continuous data related to every
(PI), aims to use technology to collect personal data on different aspects of people’s daily life opens new opportunities for behavior
aspects of people’s daily lives. QS tools, defined as “those that change design, including the potential for more personalized, just-
help people collect personally relevant information for the in-time and effective interventions, based on the knowledge of the
purpose of self-reflection and gaining self-knowledge” [10], allow whole range of the individuals’ activities, in order to support
individuals to self-monitor, facilitating new ways to gain self- behavior modification.
awareness. However, these technologies can also be used to
remember episodes related to one’s own personal experience and Another field of application of QS tools refers to the possibility of
to produce change in behavior. The diverse sensors also offer rich designing for remembering. The CHI community has, for some
potential to enhance learning in many contexts, from formal years, engaged in supporting people in capturing and recovering
education to lifelong learning. personal memories. Mobile and wearable technologies (e.g.
MyLifeBits, Eyetap, Narrative Clips) have been designed to
Building on our previous successful experiences in organizing capture comprehensive records of a person’s experiences,
PI/QS workshops (e.g. at CHI 2010-13, BIBM 2014, UbiComp enabling a form of “total recall” of the past [6]. Van den Hoven et
2014-15), which gathered a large and unexpected number of al. [14] reviewed how researchers have explored the role of HCI
papers related to the collection and use of personal data, in this in designing for personal memories, developing novel devices for
workshop we want to explore how we can design for QS remembering or for supporting recollection with memory aids. QS
improving its effectiveness in specific domains, i.e. to trigger technologies can now go further in this direction, enriching the
changes in behavior, help people remember their past and improve retrieval process of personal memories with a plethora of
their broader learning. contextual data, transparently collected during everyday activities,
to support the user’s reflection on the choices she made, her past
behavior and objectives, and, through these, providing insights
about her potential future options.
Finally, there are many learning contexts where emerging sensors
can play valuable roles. In formal education settings, personal
data can provide a potentially motivating context for mathematics
and personal development and health studies. In learning a
complex skill, video capture technologies could support review of
work episodes to facilitate gaining post-hoc review of interesting
performance episodes, be they ones that proved to be very
effective, or problematic. One other class of long term personal 3. SHORT BIO OF THE ORGANIZERS
data capture may be in the context of mastering a skill that takes Amon Rapp (main contact). Research fellow at Computer
years. Data about this may be collected from diverse apps that Science Department of the University of Torino, where he directs
support this learning and each capture data reflecting progress in the Smart Personal Technology Lab. His research areas are QS
learning. In learning contexts, an Open Learner Model - OLM [2] and behavior change technologies, investigated from an HCI
provides an interface to such data. There are important challenges perspective.
in creating OLMs that support the range of key metacognitive
processes of goal setting, self-monitoring and self-reflection [3]. Federica Cena. Assistant Professor at the Department of
Computer Science of the University of Torino. She is currently
As the current availability on the market of wearables and mobile the head of Smart Society Lab at the Center for Innovation for the
applications for self-tracking is making it plausible that QS Territory. She is working on user modeling and personalization,
technologies will become pervasive in the near future, we have to with a special focus on the implications of IoT for user modeling.
start to explore how to employ personal data effectively, in
different domains and for a broad user base. In fact, many issues Judy Kay. Professor of Computer Science at the University of
still remain for the daily use of these technologies, mainly related Sydney, Australia. She heads the Human Centred Technology
to the continuity and the accuracy of the data tracking, the ability priority research cluster. Her primary research focus is on surface
to merge various sources of personal information, and the computing and infrastructures for managing personal data with the
meaningfulness of the interfaces and visualizations provided [5]. user in control. Key applications are in life-long and life-wide
While many dedicated “quantified selfers” can overcome these learning, with data supporting metacognitive processes, including
problems because of their familiarity with self-tracking reflection and goal setting.
technologies and a strong motivation to track their own behaviors Bob Kummerfeld. Associate Professor of Computer Science at
[4], the broader population does not have such skill, experience the University of Sydney, Australia. His research is mainly on
and willingness to overcome current hurdles to collecting and systems for the management of User Model data as well as novel
manipulating personal data. interfaces for gathering and managing personal data.
It is necessary, then, to try to rethink the design of these tools, Frank Hopfgartner. Lecturer in Information Studies at
making them better fit the needs and desires of this new kind of University of Glasgow. His research to date can be placed in the
potential users, and to explore which benefits they could be intersection of information retrieval, recommender systems, and
provided in the future. For example, in regard to data tracking, data analytics. He co-organized various workshops on
although many improvements will come from the advances in heterogeneous sensor data, Quantified Self and Lifelogging (e.g.,
wearable technologies, many problems will persist, related, for at ICME, UMAP, Hypertext, BIBM) and is co-chair of Lifelog, a
example, to the collection of complex states or events, such as the pilot task for the evaluation of lifelogging and retrieval techniques
user’s cognitive and emotional states, or the important episodes at NTCIR-12.
she experiences in her everyday life. For these data, it is essential
Jakob Eg Larsen. Associate Professor in Cognitive Systems at
to imagine new design techniques that can improve the user’s
the Technical University of Denmark, Dept. of Applied
motivation in reporting them.
Mathematics and Computer Science, where he heads the Mobile
However, lightening the burden of self-tracking will not be Informatics and Personal Data Lab. His research interests include
sufficient if it is not paired with an enhancement in the perceived HCI, personal data interaction, data visualization, personal
benefits that all these personal collected data could provide [11]. informatics and quantified self. He has organized several
The CHI community should then find new ways for making them workshops on personal informatics and quantified self.
more understandable, actionable and effective in reaching
Elise van den Hoven. Associate Professor in the School of
concrete purposes. We believe that useful applications of QS
Design at University of Technology Sydney and part-time
technologies can be found in technologies for behavior change,
associate professor in the Department of Industrial Design,
memory and learning. Addressing some of the design challenges
Eindhoven University of Technology. She has two honorary
that QS tools are currently facing, they could help users in
appointments: honorary senior research fellow in Duncan of
modifying a undesired habit, relive their past through an enriched
Jordanstone College of Art and Design, University of Dundee and
experience, and improve their learning processes. For example,
associate investigator with the Australian Research Council's
designing effective tools for the management of the data tracked is
Centre of Excellence in Cognition and its Disorders. Her research
crucial to provide users with a comprehensive and understandable
interests span different disciplines, including human-computer
mirror of themselves, able to enhance their self-awareness and
interaction, design and psychology, including people-centred
trigger processes of change.
design, designing interactive systems, physical interaction and
On the other hand, understanding how to model users’ habits and supporting human remembering.
everyday activities, for example through user modeling techniques
[1] based on real-world data, could provide each user with 4. ACCEPTED PAPERS
personalized feedback and recommendations, going beyond a one- 1. Marieke M.M. Peeters & Mark A. Neerincx. Human-Agent
size-fits-all approach, which has already showed its limits [8]. Experience Sharing: Creating Social Agents for Elderly People
Moreover, selecting significant contextual details of an event, with Dementia.
connected to the user’s emotional experience, and finding new 2. Nabil Bin Hannan, Felwah Alqahtani, & Derek Reilly.
ways to represent the data collected could improve the JogChalking: Capturing and Visualizing Affective Experience for
reminiscence, enabling users to relive their past episodes and Recreational Runners
recall the emotions connected to them.
3. Amon Rapp, Alessandro Marcengo, & Federica Cena.
Accuracy and Reliability of Personal Data Collection: An
Autoethnographic Study
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