earticle

영어

User authentication is a key element of security systems, requiring technologies that enhance efficiency and reliability. Although traditional fingerprint recognition is highly reliable, it requires user participation for authentication, which reduces its efficiency. To address this issue, non-intrusive and highly reliable biometric technologies, such as iris recognition, are gaining attention. In this paper, we propose a wristwatchtype biometric authentication system that utilizes electromyogram (EMG) signals, which are easy to implement in wearable systems, along with artificial intelligence (AI) hardware accelerator technology. To achieve this, a fieldprogrammable gate array (FPGA)-based hardware accelerator was utilized, with the Python on Zynq (PYNQ) platform specifically employed to maximize parallel processing capabilities and enhance the performance of the user authentication system. EMG signals were acquired through a wristwatch-type EMG sensor with two channels, and signal processing was conducted using the empirical mode decomposition (EMD) method. The artificial intelligence network employed a convolutional neural network (CNN)-long short-term memory (LSTM) architecture. This approach achieved 98.7% accuracy and a 0.5 ms response time for user authentication across four users.