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      Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement

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          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.

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                Author and article information

                Contributors
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                31 July 2015
                August 2015
                : 15
                : 8
                : 18901-18933
                Affiliations
                Graduate School of Biomedical Engineering, UNSW Australia, Sydney NSW 2052, Australia; E-Mails: m.delrosario@ 123456unsw.edu.au (M.B.R.); s.redmond@ 123456unsw.edu.au (S.J.R.)
                Author notes
                [†]

                These authors contributed equally to this work.

                [* ]Author to whom correspondence should be addressed; E-Mail: N.Lovell@ 123456unsw.edu.au ; Tel.: +61-2-9385-3922; Fax: +61-2-9663-2108.
                Article
                sensors-15-18901
                10.3390/s150818901
                4570352
                26263998
                ae473732-56ee-4064-9ecb-079d028232eb
                © 2015 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 15 June 2015
                : 28 July 2015
                Categories
                Review

                Biomedical engineering
                smartphone,activity classification,algorithms,sensors,accelerometer,gyroscope,barometer,telehealth

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