Special Issue "Quantified Self and Personal Informatics"

A special issue of Computers (ISSN 2073-431X).

Deadline for manuscript submissions: closed (1 November 2017)

Special Issue Editors

Guest Editor
Dr. Alessandro Marcengo

Telecom Italia-Research and Prototyping, Turin, Italy
Website | E-Mail
Interests: human–computer interaction; cognitive and social psychology; quantified self
Guest Editor
Assist. Prof. Dr. Federica Cena

Department of Computer Science, University of Torino, Turin, Italy
Website | E-Mail
Interests: user modeling; adaptive systems; semantic web; web of things
Guest Editor
Dr. Amon Rapp

Department of Computer Science, University of Torino, Turin, Italy
Website | E-Mail
Interests: personal informatics; behavior change; games; gamification; human–computer interaction methods

Special Issue Information

Dear Colleagues,

The Parallel Session on “Quantified Self and Personal Informatics” was held in 2014 and 2015, at the International Conference on Human–Computer Interaction. The third edition of the session will be held at the 19th International Conference on Human–Computer Interaction, 9–14 July, 2017, in Vancouver, Canada. For more information about the session please use this link: https://qsandpi.wordpress.com/ and http://2017.hci.international/index.php.

Selected papers that participated to these parallel sessions are invited to submit their extended version to this Special Issue of Computers after the conference, and, at the latest, by 1 November, 2017. Submitted papers should be extended to the size of regular research or review articles, with a 50% extension of new results. All submitted papers will undergo our standard peer-review procedure. Accepted papers will be published in open access format in Computers and will be collected together in this Special Issue.

Please prepare and format your paper according to the Instructions for Authors. Use the LaTeX or Microsoft Word template file of the journal (both are available from the Instructions for Authors page). Manuscripts should be submitted online via our susy.mdpi.com editorial system.

Dr. Alessandro Marcengo
Assist. Prof. Dr. Federica Cena
Dr. Amon Rapp
Guest Editors

Keywords

  • Lifelogging
  • Quantified Self
  • Personal Informatics
  • Wearable technologies
  • Ubiquitous computing

Published Papers (6 papers)

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Editorial

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Open AccessEditorial Editorial of the Special Issue on Quantified Self and Personal Informatics
Received: 30 January 2018 / Revised: 30 January 2018 / Accepted: 1 February 2018 / Published: 2 February 2018
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Abstract
In recent years, we witnessed the spreading of a plethora of wearable and mobile technologies allowing for a continuous and “transparent” gathering of personal data [...] Full article
(This article belongs to the Special Issue Quantified Self and Personal Informatics)

Research

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Open AccessArticle Visualizing the Provenance of Personal Data Using Comics
Received: 1 November 2017 / Revised: 20 December 2017 / Accepted: 22 January 2018 / Published: 1 February 2018
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Abstract
Personal health data is acquired, processed, stored, and accessed using a variety of different devices, applications, and services. These are often complex and highly connected. Therefore, use or misuse of the data is hard to detect for people, if they are not capable
[...] Read more.
Personal health data is acquired, processed, stored, and accessed using a variety of different devices, applications, and services. These are often complex and highly connected. Therefore, use or misuse of the data is hard to detect for people, if they are not capable to understand the trace (i.e., the provenance) of that data. We present a visualization technique for personal health data provenance using comic strips. Each strip of the comic represents a certain activity, such as entering data using a smartphone application, storing or retrieving data on a cloud service, or generating a diagram from the data. The comic strips are generated automatically using recorded provenance graphs. The easy-to-understand comics enable all people to notice crucial points regarding their data such as, for example, privacy violations. Full article
(This article belongs to the Special Issue Quantified Self and Personal Informatics)
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Open AccessArticle Self-Monitoring of Emotions and Mood Using a Tangible Approach
Received: 1 November 2017 / Revised: 19 December 2017 / Accepted: 19 December 2017 / Published: 8 January 2018
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Abstract
Nowadays Personal Informatics (PI) devices are used for sensing and saving personal data, everywhere and at any time, helping people improve their lives by highlighting areas of good and bad performances and providing a general awareness of different levels of conduct. However, not
[...] Read more.
Nowadays Personal Informatics (PI) devices are used for sensing and saving personal data, everywhere and at any time, helping people improve their lives by highlighting areas of good and bad performances and providing a general awareness of different levels of conduct. However, not all these data are suitable to be automatically collected. This is especially true for emotions and mood. Moreover, users without experience in self-tracking may have a misperception of PI applications’ limits and potentialities. We believe that current PI tools are not designed with enough understanding of such users’ needs, desires, and problems they may encounter in their everyday lives. We designed and prototype the Mood TUI (Tangible User Interface), a PI tool that supports the self-reporting of mood data using a tangible interface. The platform is able to gather six different mood states and it was tested through several participatory design sessions in a secondary/high school. The solution proposed allows gathering mood values in an amusing, simple, and appealing way. Users appreciated the prototypes, suggesting several possible improvements as well as ideas on how to use the prototype in similar or totally different contexts, and giving us hints for future research. Full article
(This article belongs to the Special Issue Quantified Self and Personal Informatics)
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Open AccessArticle NEAT-Lamp and Talking Tree: Beyond Personal Informatics towards Active Workplaces
Received: 31 October 2017 / Revised: 10 December 2017 / Accepted: 15 December 2017 / Published: 28 December 2017
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Abstract
A growing number of personal informatics (PI) systems have been designed to break the habit of prolonged sitting and to encourage physical activity during workdays and leisure hours. Few studies, however, have investigated the nature of local movement and mobility in workspaces. Relatively
[...] Read more.
A growing number of personal informatics (PI) systems have been designed to break the habit of prolonged sitting and to encourage physical activity during workdays and leisure hours. Few studies, however, have investigated the nature of local movement and mobility in workspaces. Relatively little is known about how such movement patterns are shaped and in what ways micro-mobility in workplaces could be increased. By undertaking a concept-driven design approach, and on the basis of our ethnographic prestudy, we introduce a conceptual framework. In this conceptual framework, we indicate the five main agencies that shape local movement and mobility among office workers. On the basis of this empirical and conceptual work, two prototypes, the non-exercise activity thermogenesis (NEAT)-Lamp and Talking Tree, have been designed, implemented and observed in an office environment. This paper describes this design project and articulates the role of discussions in socially established settings in work environments in order to increase daily movement. The paper concludes by highlighting not only technology, but also collective reflections to spark behavioral change in office environments as social settings. Full article
(This article belongs to the Special Issue Quantified Self and Personal Informatics)
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Open AccessFeature PaperArticle Personalizing the Fitting of Hearing Aids by Learning Contextual Preferences From Internet of Things Data
Received: 1 November 2017 / Revised: 9 December 2017 / Accepted: 20 December 2017 / Published: 23 December 2017
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Abstract
The lack of individualized fitting of hearing aids results in many patients never getting the intended benefits, in turn causing the devices to be left unused in a drawer. However, living with an untreated hearing loss has been found to be one of
[...] Read more.
The lack of individualized fitting of hearing aids results in many patients never getting the intended benefits, in turn causing the devices to be left unused in a drawer. However, living with an untreated hearing loss has been found to be one of the leading lifestyle related causes of dementia and cognitive decline. Taking a radically different approach to personalize the fitting process of hearing aids, by learning contextual preferences from user-generated data, we in this paper outline the results obtained through a 9-month pilot study. Empowering the user to select between several settings using Internet of things (IoT) connected hearing aids allows for modeling individual preferences and thereby identifying distinct coping strategies. These behavioral patterns indicate that users prefer to switch between highly contrasting aspects of omnidirectionality and noise reduction dependent on the context, rather than relying on the medium “one size fits all” program frequently provided by default in hearing health care. We argue that an IoT approach facilitated by the usage of smartphones may constitute a paradigm shift, enabling continuous personalization of settings dependent on the changing context. Furthermore, making the user an active part of the fitting solution based on self-tracking may increase engagement and awareness and thus improve the quality of life for hearing impaired users. Full article
(This article belongs to the Special Issue Quantified Self and Personal Informatics)
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Open AccessFeature PaperArticle Promises and Pitfalls of Computer-Supported Mindfulness: Exploring a Situated Mobile Approach
Received: 1 November 2017 / Revised: 19 December 2017 / Accepted: 21 December 2017 / Published: 22 December 2017
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Abstract
Computer-supported mindfulness (CSM) is a burgeoning area filled with varied approaches such as mobile apps and EEG headbands. However, many of the approaches focus on providing meditation guidance. The ubiquity of mobile devices may provide new opportunities to support mindfulness practices that are
[...] Read more.
Computer-supported mindfulness (CSM) is a burgeoning area filled with varied approaches such as mobile apps and EEG headbands. However, many of the approaches focus on providing meditation guidance. The ubiquity of mobile devices may provide new opportunities to support mindfulness practices that are more situated in everyday life. In this paper, a new situated mindfulness approach is explored through a specific mobile app design. Through an experimental design, the approach is compared to traditional audio-based mindfulness meditation, and a mind wandering control, over a one-week period. The study demonstrates the viability for a situated mobile mindfulness approach to induce mindfulness states. However, phenomenological aspects of the situated mobile approach suggest both promises and pitfalls for computer-supported mindfulness using a situated approach. Full article
(This article belongs to the Special Issue Quantified Self and Personal Informatics)
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