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The digital health companion: Personalized health support on smartwatches via recognition of activity- and vital-data

: Trimpop, John; Haescher, Marian; Bieber, Gerald; Matthies, Denys J.C.; Lämmel, Friedrich; Burggraf, Paul

Fulltext urn:nbn:de:0011-n-3749163 (73 KByte PDF) - This publication has been replaced by a revised version.
MD5 Fingerprint: 57e0418c1b54e56234be7e01a0a86a4b
Created on: 13.2.2016

Fulltext urn:nbn:de:0011-n-374916-12 (218 KByte PDF) - Updated version
MD5 Fingerprint: 989988e392730da18b924cff38421c16
Created on: 27.2.2016

Schulz, Hans-Jörg (Hrsg.); Urban, Bodo (Hrsg.); Lukas, Uwe von (Hrsg.) ; Fraunhofer-Institut für Graphische Datenverarbeitung -IGD-, Institutsteil Rostock; Univ. Rostock:
Proceedings of the International Summer School on Visual Computing 2015 : August 17-21, 2015 in Rostock, Germany
Stuttgart: Fraunhofer Verlag, 2015
ISBN: 978-3-8396-0960-6
International Summer School on Visual Computing <1, 2015, Rostock>
Conference Paper, Electronic Publication
Fraunhofer IGD ()
Fraunhofer IGD-R ()
activity recognition; E-health; emergency management; inertial sensors; smart watches

It has been shown that in various fields of social life, people tend to seek opportunities to measure their daily activities, bodily behaviors, and health related parameters. These kinds of activity tracking should be accomplished comfortably, unobtrusively and implicitly. Tracking behavior can be important for certain user groups, such as the growing population of elderlies. These people have a substantially higher risk of falling down, as they often live alone and thus have a greater need for other supporting services, as emergencies quickly occur.
We would like to support these people, while providing a comfortable emergency detection and a monitoring of physical activities. Moreover, we believe such tracking applications to be beneficial for any user group, since we can perceive the trend of quantified self: knowing about one's own body characteristics, which is expressed in body movement. Simultaneously, we also perceive that a strong desire for a comprehensive monitoring of vital and health data is emerging. In this paper we describe the concept and implementation of the Digital Health Companion, a smart health support system that combines research developments of activity, vital data, and anomaly recognition with the functionality of contemporary smartwatches. The system's health monitoring includes an emergency detection and allows for the prevention of health risks in the short and long term through the recognition of body movement patterns.