{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T14:50:18Z","timestamp":1781621418127,"version":"3.54.5"},"reference-count":26,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Biometric identification systems are a fundamental building block of modern security. However, conventional biometric methods cannot easily cope with their intrinsic security liabilities, as they can be affected by environmental factors, can be easily \u201cfooled\u201d by artificial replicas, among other caveats. This has lead researchers to explore other modalities, in particular based on physiological signals. Electrocardiography (ECG) has seen a growing interest, and many ECG-enabled security identification devices have been proposed in recent years, as electrocardiography signals are, in particular, a very appealing solution for today\u2019s demanding security systems\u2014mainly due to the intrinsic aliveness detection advantages. These Electrocardiography (ECG)-enabled devices often need to meet small size, low throughput, and power constraints (e.g., battery-powered), thus needing to be both resource and energy-efficient. However, to date little attention has been given to the computational performance, in particular targeting the deployment with edge processing in limited resource devices. As such, this work proposes an implementation of an Artificial Intelligence (AI)-enabled ECG-based identification embedded system, composed of a RISC-V based System-on-a-Chip (SoC). A Binary Convolutional Neural Network (BCNN) was implemented in our SoC\u2019s hardware accelerator that, when compared to a software implementation of a conventional, non-binarized, Convolutional Neural Network (CNN) version of our network, achieves a 176,270\u00d7 speedup, arguably outperforming all the current state-of-the-art CNN-based ECG identification methods.<\/jats:p>","DOI":"10.3390\/s22010348","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"348","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1919-4661","authenticated-orcid":false,"given":"Francisco","family":"de Melo","sequence":"first","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), 1049-001 Lisbon, Portugal"},{"name":"Instituto Superior T\u00e9cnico (IST), Universidade de Lisboa (UL), 1050-049 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3621-8322","authenticated-orcid":false,"given":"Hor\u00e1cio C.","family":"Neto","sequence":"additional","affiliation":[{"name":"Instituto Superior T\u00e9cnico (IST), Universidade de Lisboa (UL), 1050-049 Lisbon, Portugal"},{"name":"Instituto de Engenharia de Sistemas e Computadores (INESC)\u2014Investiga\u00e7\u00e3o e Desenvolvimento (ID), 1000-029 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6764-8432","authenticated-orcid":false,"given":"Hugo Pl\u00e1cido","family":"da Silva","sequence":"additional","affiliation":[{"name":"Instituto de Telecomunica\u00e7\u00f5es (IT), 1049-001 Lisbon, Portugal"},{"name":"Instituto Superior T\u00e9cnico (IST), Universidade de Lisboa (UL), 1050-049 Lisbon, Portugal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"da Silva, H.P., Fred, A., Louren\u00e7o, A., and Jain, A.K. 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