Classification of data and activities in self-quantification systems

Manal Almalki, Guillermo Lopez-Campos, Kathleen Gray, Fernando J Martin-Sanchez

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Self-quantification may be seen as an emerging paradigm for health data. In recent years the general public has become more health-conscious, due in part to the self-tracking and quantification technologies that enable the non-expert to easily capture and share significant health-related information on a daily basis (Mehta, 2011). This self-tracking of personal health and fitness data has the potential to introduce new research methods in citizen science, and in formal research into personalised medicine and healthcare (Swan, 2009). Such methods capture data in real tasks, natural settings, and in situ, as well as facilitate the measurement of some health and life aspects longitudinally, with an aim of generating healthcare-related hypotheses. However, this field lacks a systematic approach to classifying these data, and making sense of these observational measurements. This paper reports on our data classification model, and how it can be used in data collection, data analysis, data curation and data exchange.

Original languageEnglish
Pages (from-to)18-19
Number of pages2
JournalCEUR Workshop Proceedings
Volume1149
Publication statusPublished - 03 Apr 2014
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)

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