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

被引：28

作者：

Sztyler, Timo ^{[1
]}

Carmona, Josep ^{[2
]}

Voelker, Johanna ^{[1
]}

Stuckenschmidt, Heiner ^{[1
]}

机构：

[1] Univ Mannheim, Mannheim, Germany

[2] Univ Politecn Cataluna, Barcelona, Spain

来源：

TRANSACTIONS ON PETRI NETS AND OTHER MODELS OF CONCURRENCY XI | 2016年 / 9930卷

关键词：

ACTIVITY RECOGNITION; PROCESS MODELS; ACCELERATION; CONFORMANCE;

D O I：

10.1007/978-3-662-53401-4_8

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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.

引用

页码：160 / 180

页数：21

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