A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults
ITC/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, NV === Falls prevention efforts for older adults have become increasingly important and are now a...
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ndltd-arizona.edu-oai-arizona.openrepository.com-10150-5963772016-02-18T03:00:39Z A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults Martinez, Matthew De Leon, Phillip L. New Mexico State University Sandia National Laboratories Falls risk gait accelerometer machine learning smartphone app ITC/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, NV Falls 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. 2015-10 text Proceedings 0884-5123 0074-9079 http://hdl.handle.net/10150/596377 http://arizona.openrepository.com/arizona/handle/10150/596377 International Telemetering Conference Proceedings en_US http://www.telemetry.org/ Copyright © held by the author; distribution rights International Foundation for Telemetering International Foundation for Telemetering |
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en_US |
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Falls risk gait accelerometer machine learning smartphone app |
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Falls risk gait accelerometer machine learning smartphone app Martinez, Matthew De Leon, Phillip L. A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults |
description |
ITC/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, NV === Falls 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. |
author2 |
New Mexico State University |
author_facet |
New Mexico State University Martinez, Matthew De Leon, Phillip L. |
author |
Martinez, Matthew De Leon, Phillip L. |
author_sort |
Martinez, Matthew |
title |
A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults |
title_short |
A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults |
title_full |
A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults |
title_fullStr |
A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults |
title_full_unstemmed |
A Smartphone-Based Gait Data Collection System for the Prediction of Falls in Elderly Adults |
title_sort |
smartphone-based gait data collection system for the prediction of falls in elderly adults |
publisher |
International Foundation for Telemetering |
publishDate |
2015 |
url |
http://hdl.handle.net/10150/596377 http://arizona.openrepository.com/arizona/handle/10150/596377 |
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