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System-on-Chip Solution for Patients Biometric: A Compressive Sensing-Based Approach

Djelouat, Hamza and Zhai, Xiaojun and Al Disi, Mohamed and Amira, Abbes and Bensaali, Faycal (2018) 'System-on-Chip Solution for Patients Biometric: A Compressive Sensing-Based Approach.' IEEE Sensors Journal, 18 (23). pp. 9629-9639. ISSN 1530-437X

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IEEE The ever-increasing demand for biometric solutions for the internet of thing (IoT)-based connected health applications is mainly driven by the need to tackle fraud issues, along with the imperative to improve patient privacy, safety and personalized medical assistance. However, the advantages offered by the IoT platforms come with the burden of big data and its associated challenges in terms of computing complexity, bandwidth availability and power consumption. This paper proposes a solution to tackle both privacy issues and big data transmission by incorporating the theory of compressive sensing (CS) and a simple, yet, efficient identification mechanism using the electrocardiogram (ECG) signal as a biometric trait. Moreover, the paper presents the hardware implementation of the proposed solution on a system on chip (SoC) platform with an optimized architecture to further reduce hardware resource usage. First, we investigate the feasibility of compressing the ECG data while maintaining a high identification quality. The obtained results show a 98.88% identification rate using only a compression ratio of 30%. Furthermore, the proposed system has been implemented on a Zynq SoC using heterogeneous software/hardware solution, which is able to accelerate the software implementation by a factor of 7.73 with a power consumption of 2.318 W.

Item Type: Article
Uncontrolled Keywords: Internet of Things (IoT); compressive sensing (CS); zynq SoC; reconstruction algorithms; pattern recognition
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 11 Oct 2018 14:16
Last Modified: 15 Jan 2022 01:25

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