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An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

Author

Listed:
  • Farhad Ahamed

    (School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW 2747, Australia)

  • Farnaz Farid

    (School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW 2747, Australia)

  • Basem Suleiman

    (School of Computer Science, The University of Sydney, Sydney, NSW 2008, Australia)

  • Zohaib Jan

    (Boeing Defence Australia, Brisbane, QLD 4000, Australia)

  • Luay A. Wahsheh

    (Department of Computer Science and Information Systems, University of North Georgia, Dahlonega, GA 30597, USA)

  • Seyed Shahrestani

    (School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW 2747, Australia)

Abstract

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16.

Suggested Citation

  • Farhad Ahamed & Farnaz Farid & Basem Suleiman & Zohaib Jan & Luay A. Wahsheh & Seyed Shahrestani, 2022. "An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services," Future Internet, MDPI, vol. 14(8), pages 1-28, July.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:8:p:222-:d:872041
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    References listed on IDEAS

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    1. Diego di Bernardo & Alan Murray, 2000. "Explaining the T-wave shape in the ECG," Nature, Nature, vol. 403(6765), pages 40-40, January.
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    Cited by:

    1. C. Annadurai & I. Nelson & K. Nirmala Devi & R. Manikandan & N. Z. Jhanjhi & Mehedi Masud & Abdullah Sheikh, 2022. "Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City," Energies, MDPI, vol. 15(19), pages 1-14, October.

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