Abstract
BACKGROUND: Microwave Doppler sensors, capable of detecting minute physiological movements, enable the measurement of biometric information, such as walking patterns, heart rate, and respiration. Unlike fingerprint and facial recognition systems, they offer authentication without physical contact or privacy concerns. This study focuses on non-contact seismocardiography using microwave Doppler sensors and aims to apply this technology for biometric authentication. METHOD: We proposed a method for authenticating and identifying heartbeat signals through supervised learning using a conditional variational autoencoder (CVAE). A 60 GHz microwave Doppler sensor was used to capture heartbeat signals, which were processed using a conformer network to detect peaks and segment individual beats. High signal-to-noise ratio waveforms were selected, and time-frequency analysis extracted relevant features. Spectrograms labeled with subject data were input into the CVAE, which encoded subject-specific features into a latent space for authentication. RESULTS: The proposed heartbeat-based authentication method, validated on 13 subjects, achieved an average balanced accuracy of 97.3% for authentication and an average accuracy of 94.7% for identification. Compared with conventional methods, this approach demonstrated superior performance by effectively encoding subject-specific features while mitigating noise-related challenges. CONCLUSION: The proposed method enhanced the feasibility of non-contact heartbeat-based authentication by achieving high accuracy while addressing noise-related challenges. Its application could improve biometric security without compromising user privacy. Further advancements in handling posture variations and scalability are essential for real-world implementation.