DL-Aided NOMP: a Deep Learning-Based Vital Sign Estimating Scheme Using FMCW Radar

Author(s):  
Hsin-Yuan Chang ◽  
Chia-Hung Lin ◽  
Yu-Chien Lin ◽  
Wei-Ho Chung ◽  
Ta-Sung Lee
Keyword(s):  
2019 ◽  
Vol 67 (8) ◽  
pp. 5073-5080 ◽  
Author(s):  
Arnau Prat ◽  
Sebastian Blanch ◽  
Albert Aguasca ◽  
Jordi Romeu ◽  
Antoni Broquetas

Author(s):  
Jose-Maria Munoz-Ferreras ◽  
Jing Wang ◽  
Zhengyu Peng ◽  
Changzhi Li ◽  
Roberto Gomez-Garcia

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2166
Author(s):  
Kyungeun Park ◽  
Jeongpyo Lee ◽  
Youngok Kim

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.


Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6505
Author(s):  
Emmi Turppa ◽  
Juha M. Kortelainen ◽  
Oleg Antropov ◽  
Tero Kiuru

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of 3.6% (86% correlation) and 9.1% (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses.


2019 ◽  
Vol 40 (11) ◽  
pp. 115001 ◽  
Author(s):  
Sitthichok Chaichulee ◽  
Mauricio Villarroel ◽  
João Jorge ◽  
Carlos Arteta ◽  
Kenny McCormick ◽  
...  

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