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Sensors 2015, 15(8), 18901-18933; doi:10.3390/s150818901

Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement

Graduate School of Biomedical Engineering, UNSW Australia, Sydney NSW 2052, Australia
These authors contributed equally to this work.
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Author to whom correspondence should be addressed.
Academic Editor: Ki H. Chon
Received: 15 June 2015 / Revised: 27 July 2015 / Accepted: 28 July 2015 / Published: 31 July 2015
(This article belongs to the Special Issue Smartphone-Based Sensors for Non-Invasive Physiological Monitoring)
View Full-Text   |   Download PDF [1890 KB, uploaded 31 July 2015]   |  

Abstract

Advances in mobile technology have led to the emergence of the “smartphone”, a new class of device with more advanced connectivity features that have quickly made it a constant presence in our lives. Smartphones are equipped with comparatively advanced computing capabilities, a global positioning system (GPS) receivers, and sensing capabilities (i.e., an inertial measurement unit (IMU) and more recently magnetometer and barometer) which can be found in wearable ambulatory monitors (WAMs). As a result, algorithms initially developed for WAMs that “count” steps (i.e., pedometers); gauge physical activity levels; indirectly estimate energy expenditure and monitor human movement can be utilised on the smartphone. These algorithms may enable clinicians to “close the loop” by prescribing timely interventions to improve or maintain wellbeing in populations who are at risk of falling or suffer from a chronic disease whose progression is linked to a reduction in movement and mobility. The ubiquitous nature of smartphone technology makes it the ideal platform from which human movement can be remotely monitored without the expense of purchasing, and inconvenience of using, a dedicated WAM. In this paper, an overview of the sensors that can be found in the smartphone are presented, followed by a summary of the developments in this field with an emphasis on the evolution of algorithms used to classify human movement. The limitations identified in the literature will be discussed, as well as suggestions about future research directions. View Full-Text
Keywords: smartphone; activity classification; algorithms; sensors; accelerometer; gyroscope; barometer; telehealth smartphone; activity classification; algorithms; sensors; accelerometer; gyroscope; barometer; telehealth
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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

del Rosario, M.B.; Redmond, S.J.; Lovell, N.H. Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement. Sensors 2015, 15, 18901-18933.

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