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Sensors 2015, 15(10), 26783-26800; doi:10.3390/s151026783

Can Smartwatches Replace Smartphones for Posture Tracking?

1
Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
2
Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA
3
School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
4
Computer Science Department, El Camino College, Torrance, CA 90506, USA
5
Computer Science Department, University of Alabama Birmingham, Birmingham, AL 35233, USA
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Steffen Leonhardt and Daniel Teichmann
Received: 14 July 2015 / Revised: 15 October 2015 / Accepted: 16 October 2015 / Published: 22 October 2015
(This article belongs to the Special Issue Noninvasive Biomedical Sensors)
View Full-Text   |   Download PDF [3851 KB, uploaded 22 October 2015]   |  

Abstract

This paper introduces a human posture tracking platform to identify the human postures of sitting, standing or lying down, based on a smartwatch. This work develops such a system as a proof-of-concept study to investigate a smartwatch’s ability to be used in future remote health monitoring systems and applications. This work validates the smartwatches’ ability to track the posture of users accurately in a laboratory setting while reducing the sampling rate to potentially improve battery life, the first steps in verifying that such a system would work in future clinical settings. The algorithm developed classifies the transitions between three posture states of sitting, standing and lying down, by identifying these transition movements, as well as other movements that might be mistaken for these transitions. The system is trained and developed on a Samsung Galaxy Gear smartwatch, and the algorithm was validated through a leave-one-subject-out cross-validation of 20 subjects. The system can identify the appropriate transitions at only 10 Hz with an F-score of 0.930, indicating its ability to effectively replace smart phones, if needed. View Full-Text
Keywords: activity recognition; smartwatch; posture tracking; wireless health; machine learning; embedded medical systems activity recognition; smartwatch; posture tracking; wireless health; machine learning; embedded medical systems
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mortazavi, B.; Nemati, E.; VanderWall, K.; Flores-Rodriguez, H.G.; Cai, J.Y.J.; Lucier, J.; Naeim, A.; Sarrafzadeh, M. Can Smartwatches Replace Smartphones for Posture Tracking? Sensors 2015, 15, 26783-26800.

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