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  4. SmartMove: A smartwatch algorithm to distinguish between high- and low-amplitude motions as well as doffed-states by utilizing noise and sleep
 
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2016
Conference Paper
Title

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

Abstract
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).
Author(s)
Haescher, Marian  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Trimpop, John
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Bieber, Gerald  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Urban, Bodo  
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Mainwork
iWOAR 2016, 3rd international Workshop on Sensor-based Activity Recognition and Interaction. Proceedings  
Conference
International Workshop on Sensor-based Activity Recognition (iWOAR) 2016  
DOI
10.1145/2948963.2948964
File(s)
N-422264.pdf (1.94 MB)
Rights
Under Copyright
Language
English
Fraunhofer-Institut für Graphische Datenverarbeitung IGD  
Keyword(s)
  • activity monitoring

  • activity recognition

  • motion

  • wearable computing

  • smart watches

  • Lead Topic: Individual Health

  • Research Line: Human computer interaction (HCI)

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