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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Veröffentlicht in:	IEEE transactions on aerospace and electronic systems Jg. 54; H. 4; S. 1709 - 1723
Hauptverfasser:	Seyfioglu, Mehmet Saygin, Ozbayoglu, Ahmet Murat, Gurbuz, Sevgi Zubeyde
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2018 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
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Abstract	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.
AbstractList	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. 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.
Author	Seyfioglu, Mehmet Saygin Ozbayoglu, Ahmet Murat Gurbuz, Sevgi Zubeyde
Author_xml	– sequence: 1 givenname: Mehmet Saygin surname: Seyfioglu fullname: Seyfioglu, Mehmet Saygin email: msseyfioglu@etu.edu.tr organization: TOBB University of Economics and Technology, Ankara, Turkey – sequence: 2 givenname: Ahmet Murat surname: Ozbayoglu fullname: Ozbayoglu, Ahmet Murat email: mozbayoglu@etu.edu.tr organization: TOBB University of Economics and Technology, Ankara, Turkey – sequence: 3 givenname: Sevgi Zubeyde orcidid: 0000-0001-7487-9087 surname: Gurbuz fullname: Gurbuz, Sevgi Zubeyde email: szgurbuz@ua.edu organization: University of Alabama, Tuscaloosa, AL, USA
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Snippet	Radar-based activity recognition is a problem that has been of great interest due to applications such as border control and security, pedestrian...
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SubjectTerms	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
Title	Deep convolutional autoencoder for radar-based classification of similar aided and unaided human activities
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