Can smartwatches replace smartphones for posture tracking?

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 syst...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 15; no. 10; pp. 26783 - 26800
Main Authors:	Mortazavi, Bobak, Nemati, Ebrahim, VanderWall, Kristina, Flores-Rodriguez, Hector G, Cai, Jun Yu Jacinta, Lucier, Jessica, Naeim, Arash, Sarrafzadeh, Majid
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 22-10-2015 MDPI
Subjects:	Accuracy activity recognition Adult Algorithms Cancer Computer science embedded medical systems Energy consumption Frailty Human Humans Machine learning Monitoring systems Monitoring, Ambulatory - instrumentation Monitoring, Ambulatory - methods Movements Patients Posture Posture - physiology posture tracking Remote monitoring Sensors Smartphone Smartphones smartwatch Smartwatches Telemedicine - instrumentation Tracking Watches Wearable computers Weightlifting wireless health Young Adult Los Angeles California United States > US California smartwatch posture tracking embedded medical systems machine learning wireless health activity recognition
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work.
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s151026783