{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T16:04:48Z","timestamp":1782317088389,"version":"3.54.5"},"reference-count":62,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T00:00:00Z","timestamp":1658793600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>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\u2019s 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\u2019 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.<\/jats:p>","DOI":"10.3390\/fi14080222","type":"journal-article","created":{"date-parts":[[2022,7,26]],"date-time":"2022-07-26T22:03:42Z","timestamp":1658873022000},"page":"222","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["An Intelligent Multimodal Biometric Authentication Model for Personalised Healthcare Services"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3400-6799","authenticated-orcid":false,"given":"Farhad","family":"Ahamed","sequence":"first","affiliation":[{"name":"School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW 2747, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6335-1885","authenticated-orcid":false,"given":"Farnaz","family":"Farid","sequence":"additional","affiliation":[{"name":"School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW 2747, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2674-0253","authenticated-orcid":false,"given":"Basem","family":"Suleiman","sequence":"additional","affiliation":[{"name":"School of Computer Science, The University of Sydney, Sydney, NSW 2008, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5066-4118","authenticated-orcid":false,"given":"Zohaib","family":"Jan","sequence":"additional","affiliation":[{"name":"Boeing Defence Australia, Brisbane, QLD 4000, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luay A.","family":"Wahsheh","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Systems, University of North Georgia, Dahlonega, GA 30597, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seyed","family":"Shahrestani","sequence":"additional","affiliation":[{"name":"School of Computer, Data and Mathematical Sciences, Western Sydney University, Kingswood, NSW 2747, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,26]]},"reference":[{"key":"ref_1","unstructured":"Nor, R.M., Rahman, A.W., Sidek, K.A., and Ibrahim, A.A. 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