Ahamed, F.; Farid, F.; Suleiman, B.; Jan, Z.; Wahsheh, L.A.; Shahrestani, S. An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services. Preprints2022, 2022060223. https://doi.org/10.20944/preprints202206.0223.v1
APA Style
Ahamed, F., Farid, F., Suleiman, B., Jan, Z., Wahsheh, L.A., & Shahrestani, S. (2022). An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services. Preprints. https://doi.org/10.20944/preprints202206.0223.v1
Chicago/Turabian Style
Ahamed, F., Luay A. Wahsheh and Seyed Shahrestani. 2022 "An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services" Preprints. https://doi.org/10.20944/preprints202206.0223.v1
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. 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 publicly available datasets from 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
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
