Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities

Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even th...

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Bibliographic Details
Published in:IEEE transactions on aerospace and electronic systems Vol. 54; no. 4; pp. 1709 - 1723
Main Authors: Seyfioglu, Mehmet Saygin, Ozbayoglu, Ahmet Murat, Gurbuz, Sevgi Zubeyde
Format: Journal Article
Language:English
Published: New York IEEE 01.08.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Activity recognition
Artificial neural networks
Assisted living
Classification
Continuous wave radar
Convolutional autoencoder (CAE)
deep learning
Diagnostic systems
Feature recognition
gait recognition
Legged locomotion
Machine learning
micro-Doppler
Neural networks
Pedestrian safety
Radar
Radar signatures
Remote monitoring
Spectrogram
Support vector machines
ISSN:0018-9251, 1557-9603
Online Access:Get full text
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Summary:Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian identification for automotive safety, and remote health monitoring. This paper seeks to show the efficacy of micro-Doppler analysis to distinguish even those gaits whose micro-Doppler signatures are not visually distinguishable. Moreover, a three-layer, deep convolutional autoencoder (CAE) is proposed, which utilizes unsupervised pretraining to initialize the weights in the subsequent convolutional layers. This architecture is shown to be more effective than other deep learning architectures, such as convolutional neural networks and autoencoders, as well as conventional classifiers employing predefined features, such as support vector machines (SVM), random forest, and extreme gradient boosting. Results show the performance of the proposed deep CAE yields a correct classification rate of 94.2% for micro-Doppler signatures of 12 different human activities measured indoors using a 4 GHz continuous wave radar-17.3% improvement over SVM.
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ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2018.2799758