Physical Activity Recognition Using Continuous Wave Radar With Deep Neural Network

2020 ◽  
Author(s):  
Yiyuan Zhang ◽  
Oluwatosin J. Babarinde ◽  
Bart Vanrumste ◽  
Dominique Schreurs
Keyword(s):  
Neural Network ◽  
Physical Activity ◽  
Activity Recognition ◽  
Deep Neural Network ◽  
Continuous Wave ◽  
Wave Radar ◽  
Physical Activity Recognition

scholarly journals Deep Learning-Based Indoor Two-Dimensional Localization Scheme Using a Frequency-Modulated Continuous Wave Radar

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2166
Author(s):  
Kyungeun Park ◽  
Jeongpyo Lee ◽  
Youngok Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Continuous Wave ◽  
Two Dimensional ◽  
Fmcw Radar ◽  
Localization Scheme ◽  
Wave Radar ◽  
Conventional Scheme ◽  
24 Ghz

In this paper, we propose a deep learning-based indoor two-dimensional (2D) localization scheme using a 24 GHz frequency-modulated continuous wave (FMCW) radar. In the proposed scheme, deep neural network and convolutional neural network (CNN) models that use different numbers of FMCW radars were employed to overcome the limitations of the conventional 2D localization scheme that is based on multilateration methods. The performance of the proposed scheme was evaluated experimentally and compared with the conventional scheme under the same conditions. According to the results, the 2D location of the target could be estimated with a proposed single radar scheme, whereas two FMCW radars were required by the conventional scheme. Furthermore, the proposed CNN scheme with two FMCW radars produced an average localization error of 0.23 m, while the error of the conventional scheme with two FMCW radars was 0.53 m.

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