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Self-tracking Reloaded: Applying Process Mining to Personalized Health Care from Labeled Sensor Data

  • Timo SztylerEmail author
  • Josep Carmona
  • Johanna Völker
  • Heiner Stuckenschmidt
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9930)

Abstract

Currently, there is a trend to promote personalized health care in order to prevent diseases or to have a healthier life. Using current devices such as smart-phones and smart-watches, an individual can easily record detailed data from her daily life. Yet, this data has been mainly used for self-tracking in order to enable personalized health care. In this paper, we provide ideas on how process mining can be used as a fine-grained evolution of traditional self-tracking. We have applied the ideas of the paper on recorded data from a set of individuals, and present conclusions and challenges.

Keywords

Reference Model Activity Recognition Daily Routine Activity Label Personalized Health Care 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work as been partially supported by funds from the Ministry for Economy and Competitiveness (MINECO) of Spain and the European Union (FEDER funds) under grant COMMAS (ref. TIN2013-46181-C2-1-R).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Timo Sztyler
    • 1
    Email author
  • Josep Carmona
    • 2
  • Johanna Völker
    • 1
  • Heiner Stuckenschmidt
    • 1
  1. 1.University of MannheimMannheimGermany
  2. 2.Universitat Politècnica de CatalunyaBarcelonaSpain

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