University of Oulu

A. B. Obadi, M. Zeghid, P. L. E. Kan, P. J. Soh, M. Mercuri and O. Aldayel, "Optimized Continuous Wavelet Transform Algorithm Architecture and Implementation on FPGA for Motion Artifact Rejection in Radar-Based Vital Signs Monitoring," in IEEE Access, vol. 10, pp. 126767-126786, 2022, doi: 10.1109/ACCESS.2022.3223350

Optimized continuous wavelet transform algorithm architecture and implementation on FPGA for motion artifact rejection in radar-based vital signs monitoring

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Author: Obadi, Ameen Bin1,2; Zeghid, Medien3,4; Kan, Phak Len Eh5;
Organizations: 1Advanced Communication Engineering (ACE) CoE, Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Arau, Perlis 02600, Malaysia
2Department of Electronics and Communication Engineering, Faculty of Engineering and Petroleum, Hadhramout University, Al-Mukalla, Hadhramaut, Yemen
3Department of Computer Engineering and Networks, College of Engineering in Wadi Alddawasir, Prince Sattam Bin Abdulaziz University, Wadi Alddawasir 11991, Saudi Arabia
4Electronics and Micro-Electronics Laboratory, University of Monastir, Monastir 5000, Tunisia
5Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis, Arau, Perlis 02600, Malaysia
6Centre for Wireless Communications (CWC), University of Oulu, 90014 Oulu, Finland
7Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica (DIMES), University of Calabria, 87036 Arcavacata di Rende (CS), Italy
8Department of Electrical Engineering, King Saud University, Riyadh 11421, Saudi Arabia
9Prince Sultan Advanced Technology Research Institute (PSATRI), KSU, Riyadh 11451, Saudi Arabia
10KACST-TIC in RF and Photonics for the e-Society (RFTONICS), KSU, Riyadh 11451, Saudi Arabia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-12-16


The continuous wavelet transform (CWT) has been used in radar-based vital signs detection to identify and to remove the motion artifacts from the received radar signals. Since the CWT algorithm is computationally heavy, the processing of this algorithm typically results in long processing time and complex hardware implementation. The algorithm in its standard form typically uses software processing tools and is unable to support high-performance data processing. The aim of this research is to design an optimized CWT algorithm architecture to implement it on Field Programmable Gate Array (FPGA) in order to identify the unwanted movement introduced in the retrieved vital signs signals. The optimization approaches in the new implementation structure are based on utilizing the frequency domain processing, optimizing the required number of operations and implementing parallel processing of independent operations. Our design achieves significant processing speed and logic utilization optimization. It is found that processing the algorithm using our proposed hardware architecture is 48 times faster than processing it using MATLAB. It also achieves an improvement of 58% in speed performance compared to alternative solutions reported in literature. Moreover, efficient resources utilization is achieved and reported. This advanced performance of the proposed design is due to consciously implementing comprehensive approaches of multiple optimization techniques that results in multidimensional improvements. As a result, our achieved design is suitable for utilization in high-performance data processing applications.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 10
Pages: 126767 - 126786
DOI: 10.1109/access.2022.3223350
Type of Publication: A1 Journal article – refereed
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: The work of Ping Jack Soh is supported by the Academy of Finland 6G Flagship program (Grant no 346949).
Academy of Finland Grant Number: 346949
Detailed Information: 346949 (Academy of Finland Funding decision)
Copyright information: © The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see