Biblio
Filters: Keyword is Point of care [Clear All Filters]
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2022. Towards a Hybrid UHF RFID and NFC Platform for the Security of Medical Data from a Point of Care. 2022 IEEE 12th International Conference on RFID Technology and Applications (RFID-TA). :142–145.
In recent years, body-worn RFID and NFC (near field communication) devices have become one of the principal technologies concurring to the rise of healthcare internet of thing (H-IoT) systems. Similarly, points of care (PoCs) moved increasingly closer to patients to reduce the costs while supporting precision medicine and improving chronic illness management, thanks to timely and frequent feedback from the patients themselves. A typical PoC involves medical sensing devices capable of sampling human health, personal equipment with communications and computing capabilities (smartphone or tablet) and a secure software environment for data transmission to medical centers. Hybrid platforms simultaneously employing NFC and ultra-high frequency (UHF) RFID could be successfully developed for the first sensing layer. An application example of the proposed hybrid system for the monitoring of acute myocardial infarction (AMI) survivors details how the combined use of NFC and UHF-RFID in the same PoC can support the multifaceted need of AMI survivors while protecting the sensitive data on the patient’s health.
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2021. Discriminative Pattern Mining for Runtime Security Enforcement of Cyber-Physical Point-of-Care Medical Technology. 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC). :1066—1072.
Point-of-care diagnostics are a key technology for various safety-critical applications from providing diagnostics in developing countries lacking adequate medical infrastructure to fight infectious diseases to screening procedures for border protection. Digital microfluidics biochips are an emerging technology that are increasingly being evaluated as a viable platform for rapid diagnosis and point-of-care field deployment. In such a technology, processing errors are inherent. Cyber-physical digital biochips offer higher reliability through the inclusion of automated error recovery mechanisms that can reconfigure operations performed on the electrode array. Recent research has begun to explore security vulnerabilities of digital microfluidic systems. This paper expands previous work that exploits vulnerabilities due to implicit trust in the error recovery mechanism. In this work, a discriminative data mining approach is introduced to identify frequent bioassay operations that can be cyber-physically attested for runtime security protection.