Late Fusion Based Convolutional Network Model in Detection of Vital Signals with Radar Technology

Umut Özkaya

doi:10.29137/umagd.1231940

EN TR

Late Fusion Based Convolutional Network Model in Detection of Vital Signals with Radar Technology

Abstract

In this study, a method based on Convolutional Neural Networks (CNN) and fusion technology was proposed for the classification of vital signals. In order to obtain more information from 1-D radar signals, 2-D data were obtained with the spectrogram technique. An automated classification framework has been implemented by using pre-trained Google Net, VGG-16 and ResNet-50 models. The performance in the test data is increased by applying late fusion process to the highest performing VGG-16 and GoogleNet CNN structures. The performance of the proposed method is 92.54% Accuracy (ACC), 92.41% Sensitivity (SEN), 97.18% Specificity (SPE), 93.54% Precision (PRE), 92.66% F1-Score, and 90.25% Matthews Correlation Constant (MCC). Thanks to the proposed method, radar technology, which is one of the non-destructive detection technologies, comes to the forefront compared to wearable technologies

Keywords

Radar , Vital Sign , Deep Learning , Convolutional Neural Network , Late Fusion

Radar Teknolojisi ile Hayati Sinyallerin Tespitinde Geç Füzyon Tabanlı Evrişimsel Sinir Ağı Modeli

Öz

Bu çalışmada hayati sinyallerin sınıflandırılması için Evrişimsel Sinir Ağları (ESA) ve füzyon teknolojine dayalı bir yöntem önerildi. Tek boyutlu radar sinyallerinden daha fazla bilgi edinmek amacıyla spektrogram tekniği ile 2 boyutlu veriler elde edildi. GoogleNet, VGG-16 ve ResNet-50 ön eğitimli ESA kullanılarak otomatik bir sınıflandırma çerçevesi uygulanmıştır. En yüksek performansa sahip VGG-16 ve GoogleNet ESA yapılarına geç füzyon işlemi uygulanarak test verilerindeki performans artırılmıştır. Önerilen yöntemin performans 92.54% Doğruluk (DOĞ), 92.41% Duyarlılık (DUY), 97.18% Özgüllük (ÖZG), 93.54% Hassasiyet (HAS), 92.66% F1-Skoru ve 90.25% Matthews Korelasyon Sabiti (MKS)’dir. Önerilen yöntem sayesinde tahribatsız algılama teknolojilerinden biri olan radar teknolojisi giyilebilir teknolojilere göre daha ön plana çıkmaktadır.

Anahtar Kelimeler

Radar , Vital Sign , Deep Learning , Convolutional Neural Network , Late Fusion

References

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Details

Primary Language

English

Subjects

Electrical Engineering

Journal Section

Research Article

Authors

Umut Özkaya ^*
0000-0002-9244-0024
Türkiye

Publication Date

January 31, 2023

Submission Date

January 10, 2023

Acceptance Date

January 25, 2023

Published in Issue

Year 2023 Volume: 15 Number: 1

DOI

https://doi.org/10.29137/umagd.1231940

IZ

https://izlik.org/JA65GU39MG

APA

Özkaya, U. (2023). Late Fusion Based Convolutional Network Model in Detection of Vital Signals with Radar Technology. International Journal of Engineering Research and Development, 15(1), 248-255. https://doi.org/10.29137/umagd.1231940

Kırıkkale University, Faculty of Engineering and Natural Science, 71450 Yahşihan / Kırıkkale, Türkiye.

ijerad@kku.edu.tr

Late Fusion Based Convolutional Network Model in Detection of Vital Signals with Radar Technology

Abstract

Keywords

Radar Teknolojisi ile Hayati Sinyallerin Tespitinde Geç Füzyon Tabanlı Evrişimsel Sinir Ağı Modeli

Öz

Anahtar Kelimeler

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

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