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Sensors (Basel). 2015 Oct 22;15(10):26783-800. doi: 10.3390/s151026783.

Can smartwatches replace smartphones for posture tracking?

Author information

1
Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA. bobakm@cs.ucla.edu.
2
Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA. ebrahim@g.ucla.edu.
3
School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. KVanderWall@mednet.ucla.edu.
4
Computer Science Department, El Camino College, Torrance, CA 90506, USA. hct.flr@gmail.com.
5
Computer Science Department, University of Alabama Birmingham, Birmingham, AL 35233, USA. jacintacai@gmail.com.
6
School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. jlucier@mednet.ucla.edu.
7
School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. ANaeim@mednet.ucla.edu.
8
Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA. majid@cs.ucla.edu.
9
Department of Electrical Engineering, University of California, Los Angeles, Los Angeles, CA 90095, USA. majid@cs.ucla.edu.

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.

KEYWORDS:

activity recognition; embedded medical systems; machine learning; posture tracking; smartwatch; wireless health

PMID:
26506354
PMCID:
PMC4634473
DOI:
10.3390/s151026783
[Indexed for MEDLINE]
Free PMC Article

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