This paper presents an innovative and safe connected health solution for human identification. The system consists of the encryption and decryption of ECG signals using the advanced encryption standard (AES) as well as the recognition of individuals based on ECG biometrics. Heterogeneous and efficient implementation of the proposed system has been performed on a Xilinx ZC702 Zynq based prototyping board. Various IP-cores have been created based on the high level synthesis (HLS) implementation of the AES cipher, AES decipher and ECG identification blocks. The proposed hardware implementation has shown promising results since it met the real-time requirements and outclassed current field programmable gate array (FPGA) based systems in multiple key metrics including power consumption, processing time and hardware resources usage. The implemented system needs 10.71 ms to process one ECG sample and consumes 107mW while using only 30% of all available on-chip resources.
|Title of host publication||Proceedings - 24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016|
|Number of pages||1|
|Publication status||Published - 16 Aug 2016|
|Event||24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016 - Washington, United States|
Duration: 1 May 2016 → 3 May 2016
|Conference||24th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2016|
|Period||1/05/16 → 3/05/16|