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 finegrained 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.
Cite
CITATION STYLE
Sztyler, T., Carmona, J., Völker, J., & Stuckenschmidt, H. (2016). Self-tracking reloaded: Applying process mining to personalized health care from labeled sensor data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9930 LNCS, pp. 160–180). Springer Verlag. https://doi.org/10.1007/978-3-662-53401-4_8
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