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dc.contributor.authorMartinez, Matthewen
dc.contributor.authorDe Leon, Phillip L.en
dc.date.accessioned2016-02-16T16:19:23Zen
dc.date.available2016-02-16T16:19:23Zen
dc.date.issued2015-10en
dc.identifier.issn0884-5123en
dc.identifier.issn0074-9079en
dc.identifier.urihttp://hdl.handle.net/10150/596377en
dc.descriptionITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NVen_US
dc.description.abstractFalls prevention efforts for older adults have become increasingly important and are now a significant research effort. As part of the prevention effort, analysis of gait has become increasingly important. Data is typically collected in a laboratory setting using 3-D motion capture, which can be time consuming, invasive and requires expensive and specialized equipment as well as trained operators. Inertial sensors, which are smaller and more cost effective, have been shown to be useful in falls research. Smartphones now contain Micro Electro-Mechanical (MEM) Inertial Measurement Units (IMUs), which make them a compelling platform for gait data acquisition. This paper reports the development of an iOS app for collecting accelerometer data and an offline machine learning system to classify a subject, based on this data, as faller or non-faller based on their history of falls. The system uses the accelerometer data captured on the smartphone, extracts discriminating features, and then classifies the subject based on the feature vector. Through simulation, our preliminary and limited study suggests this system has an accuracy as high as 85%. Such a system could be used to monitor an at-risk person's gait in order to predict an increased risk of falling.
dc.description.sponsorshipInternational Foundation for Telemeteringen
dc.language.isoen_USen
dc.publisherInternational Foundation for Telemeteringen
dc.relation.urlhttp://www.telemetry.org/en
dc.rightsCopyright © held by the author; distribution rights International Foundation for Telemeteringen
dc.subjectFalls risken
dc.subjectgaiten
dc.subjectaccelerometeren
dc.subjectmachine learningen
dc.subjectsmartphone appen
dc.titleA Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adultsen_US
dc.typetexten
dc.typeProceedingsen
dc.contributor.departmentNew Mexico State Universityen
dc.contributor.departmentSandia National Laboratoriesen
dc.identifier.journalInternational Telemetering Conference Proceedingsen
dc.description.collectioninformationProceedings from the International Telemetering Conference are made available by the International Foundation for Telemetering and the University of Arizona Libraries. Visit http://www.telemetry.org/index.php/contact-us if you have questions about items in this collection.en
refterms.dateFOA2018-09-11T05:08:05Z
html.description.abstractFalls prevention efforts for older adults have become increasingly important and are now a significant research effort. As part of the prevention effort, analysis of gait has become increasingly important. Data is typically collected in a laboratory setting using 3-D motion capture, which can be time consuming, invasive and requires expensive and specialized equipment as well as trained operators. Inertial sensors, which are smaller and more cost effective, have been shown to be useful in falls research. Smartphones now contain Micro Electro-Mechanical (MEM) Inertial Measurement Units (IMUs), which make them a compelling platform for gait data acquisition. This paper reports the development of an iOS app for collecting accelerometer data and an offline machine learning system to classify a subject, based on this data, as faller or non-faller based on their history of falls. The system uses the accelerometer data captured on the smartphone, extracts discriminating features, and then classifies the subject based on the feature vector. Through simulation, our preliminary and limited study suggests this system has an accuracy as high as 85%. Such a system could be used to monitor an at-risk person's gait in order to predict an increased risk of falling.


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