Movement Compensation in Dual Continuous Wave Radar Using Deep Learning

Gomes, Gonçalo; Brás, Susana; Gouveia, Carolina; Albuquerque, Daniel; Pinho, Pedro

doi:10.3390/info16020099

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Open AccessArticle

Movement Compensation in Dual Continuous Wave Radar Using Deep Learning

by

Gonçalo Gomes

^1,*

,

Susana Brás

¹

,

Carolina Gouveia

²

,

Daniel Albuquerque

³

and

Pedro Pinho

⁴

¹

IEETA, DETI, LASI, Universidade de Aveiro, 3810-193 Aveiro, Portugal

²

AlmaScience Association—Pulp Research and Development for Smart and Sustainable Applications Madan Parque, Rua dos Inventores, 2825-182 Caparica, Portugal

³

Águeda School of Technology and Management, University of Aveiro, 3810-193 Aveiro, Portugal

⁴

Instituto de Telecomunicações, Department of Electronics, Telecommunications and Informatics (DETI), University of Aveiro, 3810-193 Aveiro, Portugal

^*

Author to whom correspondence should be addressed.

Information 2025, 16(2), 99; https://doi.org/10.3390/info16020099

Submission received: 8 January 2025 / Revised: 22 January 2025 / Accepted: 29 January 2025 / Published: 31 January 2025

(This article belongs to the Section Information Applications)

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Abstract

This work presents an innovative method for detecting the respiratory patterns of subjects walking on a treadmill, by leveraging the capabilities of deep learning (DL) technologies and a dual-radar setup. The study aims to overcome the challenge of accurately capturing respiratory rates in subjects performing body movements, a scenario less addressed in prior studies. By employing two radars operating at

5.8

GHz for motion mitigation, this study compares the efficacy of dual-radar configurations against a single-radar setup. The study employs DL algorithms based on convolutional autoencoders to mitigate the low-quality demodulated radar signals by reconstructing the respiratory signal. The models are trained with data from a single subject and data from 15 subjects, attaining average absolute errors of

0.29

and

4.59

Respiration Per Minute (RPM), respectively, allowing to conclude that the use of DL algorithms enhances the accuracy of respiratory signal detection when compared with arctangent demodulation, even in cases where radar data contain minimal information regarding vital signals.

Keywords: doppler radar; respiratory signal; deep learning; treadmill; heavy body movement

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MDPI and ACS Style

Gomes, G.; Brás, S.; Gouveia, C.; Albuquerque, D.; Pinho, P. Movement Compensation in Dual Continuous Wave Radar Using Deep Learning. Information 2025, 16, 99. https://doi.org/10.3390/info16020099

AMA Style

Gomes G, Brás S, Gouveia C, Albuquerque D, Pinho P. Movement Compensation in Dual Continuous Wave Radar Using Deep Learning. Information. 2025; 16(2):99. https://doi.org/10.3390/info16020099

Chicago/Turabian Style

Gomes, Gonçalo, Susana Brás, Carolina Gouveia, Daniel Albuquerque, and Pedro Pinho. 2025. "Movement Compensation in Dual Continuous Wave Radar Using Deep Learning" Information 16, no. 2: 99. https://doi.org/10.3390/info16020099

APA Style

Gomes, G., Brás, S., Gouveia, C., Albuquerque, D., & Pinho, P. (2025). Movement Compensation in Dual Continuous Wave Radar Using Deep Learning. Information, 16(2), 99. https://doi.org/10.3390/info16020099

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Movement Compensation in Dual Continuous Wave Radar Using Deep Learning

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