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Title: Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning

Abstract

Prior research in falls risk classification using inertial sensors has relied on the use of engineered features, which has concluded in a feature space containing hundreds of features that are likely redundant and possibly irrelevant. In this paper, we propose using fully convolutional neural networks (FCNNs) to classify older adults at low or high risk of falling using inertial sensor data collected from a smartphone. Due to the limited nature of older adult inertial gait data sets, we first pre-train the FCNN models using a publicly available data set for pedestrian activity recognition. Then via transfer learning, we train the network for falls risk classification. We reveal that via transfer learning, our falls risk classifier obtains an area under the receiver operating characteristic curve of 93.3%, which is 10.6% higher than the equivalent model trained without the use of transfer learning. Moreover, we show that our method outperforms other standard machine learning classifiers trained on features developed in prior research.

Authors:
 [1];  [2]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. New Mexico State Univ., Las Cruces, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1507516
Report Number(s):
SAND-2019-2984J
Journal ID: ISSN 2168-2194; 673515
Grant/Contract Number:  
AC04-94AL85000
Resource Type:
Accepted Manuscript
Journal Name:
IEEE Journal of Biomedical and Health Informatics
Additional Journal Information:
Journal Name: IEEE Journal of Biomedical and Health Informatics; Journal ID: ISSN 2168-2194
Publisher:
IEEE
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; Sensors; Biomedical measurement; Informatics; Time series analysis; Kernel; Biological system modeling; Data models Multi-layer neural networks; machine learning; accelerometers; gyroscopes

Citation Formats

Martinez, Matthew Thomas, and De Leon, Phillip. Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning. United States: N. p., 2019. Web. doi:10.1109/JBHI.2019.2906499.
Martinez, Matthew Thomas, & De Leon, Phillip. Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning. United States. doi:10.1109/JBHI.2019.2906499.
Martinez, Matthew Thomas, and De Leon, Phillip. Wed . "Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning". United States. doi:10.1109/JBHI.2019.2906499.
@article{osti_1507516,
title = {Falls Risk Classification of Older Adults Using Deep Neural Networks and Transfer Learning},
author = {Martinez, Matthew Thomas and De Leon, Phillip},
abstractNote = {Prior research in falls risk classification using inertial sensors has relied on the use of engineered features, which has concluded in a feature space containing hundreds of features that are likely redundant and possibly irrelevant. In this paper, we propose using fully convolutional neural networks (FCNNs) to classify older adults at low or high risk of falling using inertial sensor data collected from a smartphone. Due to the limited nature of older adult inertial gait data sets, we first pre-train the FCNN models using a publicly available data set for pedestrian activity recognition. Then via transfer learning, we train the network for falls risk classification. We reveal that via transfer learning, our falls risk classifier obtains an area under the receiver operating characteristic curve of 93.3%, which is 10.6% higher than the equivalent model trained without the use of transfer learning. Moreover, we show that our method outperforms other standard machine learning classifiers trained on features developed in prior research.},
doi = {10.1109/JBHI.2019.2906499},
journal = {IEEE Journal of Biomedical and Health Informatics},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {3}
}

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This content will become publicly available on March 27, 2020
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