An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services

Farhad Ahamed, Farnaz Farid, Basem Suleiman, Zohaib Jan, Luay A. Wahsheh and Seyed Shahrestani
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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

Future Internet, 2022, vol. 14, issue 8, 1-28

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.

Keywords: biometrics; ECG; internet of things; machine learning; personalised healthcare; PPG; smart aging; cybersecurity (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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