HCFeb 24, 2020

Imagining Data-Objects for Reflective Self-Tracking

arXiv:2002.10313v1
AI Analysis

This work addresses the challenge of making self-tracking data more meaningful for individuals and groups by integrating it into physical artifacts, though it is incremental as it builds on prior research on data-objects.

The paper tackled the problem of self-tracking data being stored without context, by studying how to design contextually relevant data-objects based on people's needs through a participatory project with five households. The result identified three key aspects for design: social sharing, contextual ambiguity, and interaction with the body, showing how an experience-centric view enhances the interplay between people, data, and objects.

While self-tracking data is typically captured real-time in a lived experience, the data is often stored in a manner detached from the context where it belongs. Research has shown that there is a potential to enhance people's lived experiences with data-objects (artifacts representing contextually relevant data), for individual and collective reflections through a physical portrayal of data. This paper expands that research by studying how to design contextually relevant data-objects based on people's needs. We conducted a participatory research project with five households using object theater as a core method to encourage participants to speculate upon combinations of meaningful objects and personal data archives. In this paper, we detail three aspects that seem relevant for designing data-objects: social sharing, contextual ambiguity and interaction with the body. We show how an experience-centric view on data-objects can contribute with the contextual, social and bodily interplay between people, data and objects.

Foundations

The foundational work for this paper's niche, ranked by how specifically the neighbourhood builds on it — not by global fame.

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