- Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi
- Vol: 15 Issue: 1
- Late Fusion Based Convolutional Network Model in Detection of Vital Signals with Radar Technology
Late Fusion Based Convolutional Network Model in Detection of Vital Signals with Radar Technology
Authors : Umut Özkaya
Pages : 248-255
View : 8 | Download : 5
Publication Date : 2023-01-31
Article Type : Research
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 technologiesKeywords : Radar, Vital Sign, Deep Learning, Convolutional Neural Network, Late Fusion