Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

SmartMove: A smartwatch algorithm to distinguish between high- and low-amplitude motions as well as doffed-states by utilizing noise and sleep

: Haescher, Marian; Trimpop, John; Bieber, Gerald; Urban, Bodo

Fulltext urn:nbn:de:0011-n-4222642 (1.9 MByte PDF)
MD5 Fingerprint: caa1bd3bde0694ed9d9b8a53b1f87cd8
© ACM This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Created on: 4.4.2018

Matthies, Denys J.C. (Ed.); Haescher, Marian (Ed.); Bieber, Gerald (Ed.); Urban, Bodo (Ed.) ; Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Computer and Human Interaction -SIGCHI-:
iWOAR 2016, 3rd international Workshop on Sensor-based Activity Recognition and Interaction. Proceedings : June 23 - 24, 2016, Fraunhofer IGD, Univerity of Rostock
New York: ACM Press, 2016 (ACM International Conference Proceedings Series 1183)
ISBN: 978-1-4503-4245-2
Art. 1, 8 pp.
International Workshop on Sensor-based Activity Recognition (iWOAR) <3, 2016, Rostock>
Conference Paper, Electronic Publication
Fraunhofer IGD, Institutsteil Rostock ()
Fraunhofer IGD ()
activity monitoring; activity recognition; motion; wearable computing; smart watches; Guiding Theme: Individual Health; Research Area: Human computer interaction (HCI)

In this paper, we describe a self adapting algorithm for smart watches to define individual transitions between motion intensities. The algorithm enables for a distinction between high-amplitude motions (e.g. walking, running, or simply moving extremities) low-amplitude motions (e.g. human microvibrations, and heart rate) as well as a general doffed-state. A prototypical implementation for detecting all three motion types was tested with a wrist-worn acceleration sensor. Since the aforementioned motion types are user-specific, SmartMove incorporates a training module based on a novel actigraphy-based sleep detection algorithm, in order to learn the specific motion types. In addition, our proposed sleep algorithm enables for reduced power consumption since it samples at a very low rate. Furthermore, the algorithm can identify suitable timeframes for an inertial sensor-based detection of vital-signs (e.g. seismocardiography or ballistocardiography).