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Application of ECG Authentication in IoT-Based Systems
|關鍵字:||ECG;心電圖;物聯網;身分認證;ECG;Electrocardiogram (EKG/ECG);Internet of Things (IoT);Identity Authentication||引用:|| A. Zanella, N. Bui, A. Castellani, L. Vangelista and M. Zorzi,'Internet of Things for Smart Cities,＂ IEEE Internet of Things Journal, vol. 1, no. 1, Feb. 2014, pp. 22-32.  S. Chatterjee et al.,'Internet of Things and Body area network-an integrated future,' 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, 2017, pp. 396-400.  P. Neumann, J. Montavont and T. Noël,'Indoor deployment of low-power wide area networks (LPWAN): A LoRaWAN case study,' 2016 IEEE 12th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), New York, 2016, pp. 1-8.  F. Ye, Y. Sun and A. Rettig,'Authentication and Access Control for an IoT Green Roof Monitoring System, ＂2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech), Orlando, FL, USA, 2017, pp. 251-256.  B. Chanv, S. Bakhru and V. Mehta,'Structural health monitoring system using IoT and wireless technologies,＂ 2017 International Conference on Intelligent Communication and Computational Techniques (ICCT), Jaipur, India, 2017, pp. 151-157.  W. Zhou, Y. Jia, A. Peng, Y. Zhang and P. Liu,'The Effect of IoT New Features on Security and Privacy: New Threats, Existing Solutions, and Challenges Yet to Be Solved, ＂ IEEE Internet of Things Journal. 2018, pp. 1-11.  I. Brass, L. Tanczer, M. Carr, M. Elsden and J. 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Lee,'Application of extension method and chaos theory in ECG identity recognition system,＂2016 IEEE 14th International Conference on Industrial Informatics (INDIN), Poitiers, 2016, pp. 1247-1251  M. H. Wang and Z. Y. Lee,'Application of extension method and chaos theory in ECG identity recognition system,＂2016 IEEE 14th International Conference on Industrial Informatics (INDIN), Poitiers, 2016, pp. 1247-1251.  S. Pathoumvanh, S. Airphaiboon, B. Prapochanung and T. Leauhatong,'ECG analysis for person identification,＂The 6th 2013 Biomedical Engineering International Conference, Amphur Muang, 2013, pp. 1-4.  F. Zeng, K. K. Tseng, H. N. Huang, S. Y. Tu and J. S. Pan,'A New Statistical-based Algorithm for ECG Identification, ＂2012 Eighth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, Piraeus, 2012, pp. 301-304.  M. Hejazi, S. A. R. Al-Haddad, S. J. Hashim, A. F. A. Aziz and Y. P. Singh, 'Feature level fusion for biometric verification with two-lead ECG signals,＂2016 IEEE 12th International Colloquium on Signal Processing & Its Applications (CSPA), Malacca City, 2016, pp. 54-59.  M. Hejazi, S. A. R. Al-Haddad, S. J. Hashim, A. F. A. Aziz and Y. P. Singh, 'Non-fiducial based ECG biometric authentication using one-class Support Vector Machine,＂2017 Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), Poznan, 2017, pp. 190-194.  Q. Zhang, D. Zhou and X. Zeng,'PulsePrint: Single-arm-ECG biometric human identification using deep learning,＂2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), New York, NY, 2017, pp. 452-456.  I. Chamatidis, A. Katsika and G. Spathoulas,'Using deep learning neural networks for ECG based authentication,＂2017 International Carnahan Conference on Security Technology (ICCST), Madrid, 2017, pp. 1-6.  P. R. Muduli, R. R. Gunukula and A. Mukherjee,'A deep learning approach to fetal-ECG signal reconstruction,＂2016 Twenty Second National Conference on Communication (NCC), Guwahati, 2016, pp. 1-6.  J. F. Wu, Y. L. Bao, S. C. Chan, H. C. Wu, L. Zhang and X. G. Wei,'Myocardial infarction detection and classification — A new multi-scale deep feature learning approach,＂2016 IEEE International Conference on Digital Signal Processing (DSP), Beijing, 2016, pp. 309-313.  Y. Yan, X. Qin, Y. Wu, N. Zhang, J. Fan and L. Wang,'A restricted Boltzmann machine based two-lead electrocardiography classification,＂2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN), Cambridge, MA, 2015, pp. 1-9.||摘要:||
With the development of the Internet of Things (IoT), many industries have gradually emerged new trends, including various trades and industries such as unmanned factories, unmanned stores, smart healthcare, and smart agriculture. When people gain the benefit of convenience, they must also face a problem at the same time. This problem is the so-called 'security', which includes various aspects such as personal privacy, corporate secrets, business transactions, and personal safety. Therefore, this study aims to build an ECG identity authentication system on the Internet of Things (IoT) system developed by the lab team to improve the overall system security.
The ECG identity authentication system uses dynamic ECG signals as the basis for identification. It has the advantage of proving that a user is a living body and capable of dynamic identification. The system is highly secure because it uses dynamic identification, making it difficult for others to copy and steal. This study uses a single-lead ECG measuring instrument to measure ECG signals. Although the accuracy is not as good as that of other multi-lead measuring instruments, it has an absolute advantage in measuring convenience. Different from the ECG identity authentications of many other published papers, this study focuses on instant measurement and instant recognition. In theory, this system has even reached the same level of fingerprint recognition by gradually recognizing the identity of a person during the measurement process.
|Appears in Collections:||電機工程學系所|
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