{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T13:44:26Z","timestamp":1792676666799,"version":"4.5.0"},"reference-count":144,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2015,7,31]],"date-time":"2015-07-31T00:00:00Z","timestamp":1438300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Advances in mobile technology have led to the emergence of the \u201csmartphone\u201d, 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 \u201ccount\u201d 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 \u201cclose the loop\u201d 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.<\/jats:p>","DOI":"10.3390\/s150818901","type":"journal-article","created":{"date-parts":[[2015,7,31]],"date-time":"2015-07-31T10:12:24Z","timestamp":1438337544000},"page":"18901-18933","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":178,"title":["Tracking the Evolution of Smartphone Sensing for Monitoring Human Movement"],"prefix":"10.3390","volume":"15","author":[{"given":"Michael","family":"Del Rosario","sequence":"first","affiliation":[{"name":"Graduate School of Biomedical Engineering, UNSW Australia, Sydney NSW 2052, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"Redmond","sequence":"additional","affiliation":[{"name":"Graduate School of Biomedical Engineering, UNSW Australia, Sydney NSW 2052, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nigel","family":"Lovell","sequence":"additional","affiliation":[{"name":"Graduate School of Biomedical Engineering, UNSW Australia, Sydney NSW 2052, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2015,7,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1249\/00005768-198315050-00010","article-title":"Estimation of energy expenditure by a portable accelerometer","volume":"15","author":"Montoye","year":"1983","journal-title":"Med. 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