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Sensors (Basel). 2016 Apr 22;16(4). pii: E570. doi: 10.3390/s16040570.

Design of Secure ECG-Based Biometric Authentication in Body Area Sensor Networks.

Author information

1
Center for Embedded and Cyber-Physical Systems, University of California, Irvine, CA 92697-3455, USA. st.peter@uci.edu.
2
Center for Embedded and Cyber-Physical Systems, University of California, Irvine, CA 92697-3455, USA. bpreddy@uci.edu.
3
Center for Embedded and Cyber-Physical Systems, University of California, Irvine, CA 92697-3455, USA. fdoktorm@uci.edu.
4
Center for Embedded and Cyber-Physical Systems, University of California, Irvine, CA 92697-3455, USA. givargis@uci.edu.

Abstract

Body area sensor networks (BANs) utilize wireless communicating sensor nodes attached to a human body for convenience, safety, and health applications. Physiological characteristics of the body, such as the heart rate or Electrocardiogram (ECG) signals, are promising means to simplify the setup process and to improve security of BANs. This paper describes the design and implementation steps required to realize an ECG-based authentication protocol to identify sensor nodes attached to the same human body. Therefore, the first part of the paper addresses the design of a body-area sensor system, including the hardware setup, analogue and digital signal processing, and required ECG feature detection techniques. A model-based design flow is applied, and strengths and limitations of each design step are discussed. Real-world measured data originating from the implemented sensor system are then used to set up and parametrize a novel physiological authentication protocol for BANs. The authentication protocol utilizes statistical properties of expected and detected deviations to limit the number of false positive and false negative authentication attempts. The result of the described holistic design effort is the first practical implementation of biometric authentication in BANs that reflects timing and data uncertainties in the physical and cyber parts of the system.

KEYWORDS:

authentication; biometric; body area sensor networks; design

PMID:
27110785
PMCID:
PMC4851084
DOI:
10.3390/s16040570
[Indexed for MEDLINE]
Free PMC Article
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