A Cloud-Based Intelligent Computing System for Contextual Exploration on Personal Sleep-Tracking Data Using Association Rule Mining

  • Zilu Liang
  • Bernd Ploderer
  • Mario Alberto Chapa Martell
  • Takuichi Nishimura
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 597)

Abstract

With the development of wearable and mobile computing technology, more and more people start using sleep-tracking tools to collect personal sleep data on a daily basis aiming at understanding and improving their sleep. While sleep quality is influenced by many factors in a person’s lifestyle context, such as exercise, diet and steps walked, existing tools simply visualize sleep data per se on a dashboard rather than analyse those data in combination with contextual factors. Hence many people find it difficult to make sense of their sleep data. In this paper, we present a cloud-based intelligent computing system named SleepExplorer that incorporates sleep domain knowledge and association rule mining for automated analysis on personal sleep data in light of contextual factors. Experiments show that the same contextual factors can play a distinct role in sleep of different people, and SleepExplorer could help users discover factors that are most relevant to their personal sleep.

Keywords

Association rules Data mining Personal informatics Sleep tracking Web applications Health Automated data analytics 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Zilu Liang
    • 1
    • 2
    • 3
  • Bernd Ploderer
    • 1
  • Mario Alberto Chapa Martell
    • 2
  • Takuichi Nishimura
    • 3
  1. 1.Department of Computing and Information SystemsUniversity of MelbourneMelbourneAustralia
  2. 2.Department of Electrical Engineering and Information SystemsThe University of TokyoTokyoJapan
  3. 3.Human Informatics Research InstituteNational Institute of Advanced Industrial Science and TechnologyTsukubaJapan

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