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Self-tracking reloaded: Applying process mining to personalized health care from labeled sensor data

Author
Sztyler, T.; Carmona, J.; Völker, J.; Stuckenschmidt, H.
Type of activity
Journal article
Journal
Lecture notes in computer science
Date of publication
2016
Volume
9930
First page
160
Last page
180
DOI
https://doi.org/10.1007/978-3-662-53401-4_8 Open in new window
Repository
http://hdl.handle.net/2117/91090 Open in new window
URL
http://link.springer.com/chapter/10.1007/978-3-662-53401-4_8 Open in new window
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 ...
Citation
Sztyler, T., Carmona, J., Völker, J., Stuckenschmidt, H. Self-tracking reloaded: Applying process mining to personalized health care from labeled sensor data. "Lecture notes in computer science", 2016, vol. 9930, p. 160-180.
Keywords
Daily lives, Health care, Personalized healthcare, Process mining, Self-tracking, Sensor data, Smartphones
Group of research
ALBCOM - Algorithms, Computational Biology, Complexity and Formal Methods

Participants

  • Sztyler, Timo  (author)
  • Carmona Vargas, Jose  (author)
  • Völker, Johanna  (author)
  • Stuckenschmidt, Heiner  (author)

Attachments