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Title: Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks

Abstract

Abstract This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.

Authors:
; ; ; ; ; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1730962
Grant/Contract Number:  
FE00029063; AC07-05ID14517
Resource Type:
Published Article
Journal Name:
Scientific Reports
Additional Journal Information:
Journal Name: Scientific Reports Journal Volume: 10 Journal Issue: 1; Journal ID: ISSN 2045-2322
Publisher:
Nature Publishing Group
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Peng, Zhaoqiang, Wen, Hongqiao, Jian, Jianan, Gribok, Andrei, Wang, Mohan, Huang, Sheng, Liu, Hu, Mao, Zhi-Hong, and Chen, Kevin P. Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks. United Kingdom: N. p., 2020. Web. https://doi.org/10.1038/s41598-020-77147-2.
Peng, Zhaoqiang, Wen, Hongqiao, Jian, Jianan, Gribok, Andrei, Wang, Mohan, Huang, Sheng, Liu, Hu, Mao, Zhi-Hong, & Chen, Kevin P. Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks. United Kingdom. https://doi.org/10.1038/s41598-020-77147-2
Peng, Zhaoqiang, Wen, Hongqiao, Jian, Jianan, Gribok, Andrei, Wang, Mohan, Huang, Sheng, Liu, Hu, Mao, Zhi-Hong, and Chen, Kevin P. Thu . "Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks". United Kingdom. https://doi.org/10.1038/s41598-020-77147-2.
@article{osti_1730962,
title = {Identifications and classifications of human locomotion using Rayleigh-enhanced distributed fiber acoustic sensors with deep neural networks},
author = {Peng, Zhaoqiang and Wen, Hongqiao and Jian, Jianan and Gribok, Andrei and Wang, Mohan and Huang, Sheng and Liu, Hu and Mao, Zhi-Hong and Chen, Kevin P.},
abstractNote = {Abstract This paper reports on the use of machine learning to delineate data harnessed by fiber-optic distributed acoustic sensors (DAS) using fiber with enhanced Rayleigh backscattering to recognize vibration events induced by human locomotion. The DAS used in this work is based on homodyne phase-sensitive optical time-domain reflectometry (φ-OTDR). The signal-to-noise ratio (SNR) of the DAS was enhanced using femtosecond laser-induced artificial Rayleigh scattering centers in single-mode fiber cores. Both supervised and unsupervised machine-learning algorithms were explored to identify people and specific events that produce acoustic signals. Using convolutional deep neural networks, the supervised machine learning scheme achieved over 76.25% accuracy in recognizing human identities. Conversely, the unsupervised machine learning scheme achieved over 77.65% accuracy in recognizing events and human identities through acoustic signals. Through integrated efforts on both sensor device innovation and machine learning data analytics, this paper shows that the DAS technique can be an effective security technology to detect and to identify highly similar acoustic events with high spatial resolution and high accuracies.},
doi = {10.1038/s41598-020-77147-2},
journal = {Scientific Reports},
number = 1,
volume = 10,
place = {United Kingdom},
year = {2020},
month = {12}
}

Journal Article:
Free Publicly Available Full Text
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https://doi.org/10.1038/s41598-020-77147-2

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