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An Artificial Neural Network Framework for Gait-Based Biometrics.

Yingnan Sun, Benny Lo

    IEEE Journal of Biomedical and Health Informatics
    |August 4, 2018
    PubMed
    Summary

    This study introduces a novel biometric cryptosystem (BCS) for securing body sensor networks (BSN). It uses gait variations and AI to generate secure keys for wearable and implantable healthcare devices.

    Area of Science:

    • Biometrics
    • Cybersecurity
    • Artificial Intelligence

    Background:

    • Growing popularity of wearable and implantable body sensor networks (BSN) raises data security concerns due to device limitations.
    • Limited computational power in BSN devices hinders robust security for sensitive physiological data.
    • Biometric cryptosystems (BCS) offer a promising solution by utilizing unique physiological and behavioral traits.

    Purpose of the Study:

    • To propose a novel biometric cryptosystem (BCS) approach for enhancing the security of wireless communications in wearable and implantable healthcare devices.
    • To develop a method for on-demand binary key generation without user intervention by extracting features from BSN sensors.
    • To evaluate the effectiveness and robustness of the proposed BCS approach using gait signal energy variations and an artificial neural network.

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    Main Methods:

    • Developed a new biometric cryptosystem (BCS) leveraging gait signal energy variations.
    • Utilized an artificial neural network framework for feature extraction and binary key generation.
    • Performed extensive analysis using a gait dataset and statistical tests (NIST, Dieharder).

    Main Results:

    • Generated binary keys with high entropy for all subjects, demonstrating strong cryptographic properties.
    • Keys successfully passed National Institute of Standards and Technology (NIST) and Dieharder statistical tests with high efficiency.
    • Demonstrated robustness through high similarity of intraclass keys and discriminability of interclass keys.

    Conclusions:

    • The proposed BCS approach effectively secures wireless communications for body sensor networks (BSN).
    • Gait signal energy variations combined with an artificial neural network provide a secure and efficient method for key generation.
    • The approach ensures data security for power-constrained wearable and implantable healthcare devices.