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

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/91090

dc.contributor	Universitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor	Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
dc.contributor.author	Sztyler, Timo
dc.contributor.author	Carmona Vargas, Josep
dc.contributor.author	Völker, Johanna
dc.contributor.author	Stuckenschmidt, Heiner
dc.date	2016
dc.identifier.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.
dc.identifier.citation	0302-9743
dc.identifier.citation	10.1007/978-3-662-53401-4_8
dc.identifier.uri	http://hdl.handle.net/2117/91090
dc.language.iso	eng
dc.relation	http://link.springer.com/chapter/10.1007/978-3-662-53401-4_8
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject	Data mining
dc.subject	Health care
dc.subject	Smartphones
dc.subject	Daily lives
dc.subject	Personalized healthcare
dc.subject	Process mining
dc.subject	Self-tracking
dc.subject	Sensor data
dc.subject	Mineria de dades
dc.title	Self-tracking reloaded: Applying process mining to personalized health care from labeled sensor data
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/article
dc.description.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.
dc.description.abstract	Peer Reviewed

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