Feasibility of biometric authentication using wearable ECG body sensor based on higher-order statistics

Besides its principal purpose in the field of biomedical applications, ECG can also serve as a biometric trait due to its unique identity properties, including user-specific deviations in ECG morphology and heart rate variability. In this paper, we exploit the possibility to use long-term ECG data acquired by unobtrusive chest-worn ECG body sensor during daily living for accurate user authentication and identification. Therefore, we propose a novel framework for wearable ECG-based user recognition. The core of the framework is based on the approach that employs higher-order statistics on cyclostationary data, already efficiently applied for inertial-sensor-based gait recognition. Experimental data was collected by four subjects during their regular daily activities with more than 6 hours of ECG data per subject and then applied to the proposed framework. Preliminary results (equal error rate from 6% to 13%, depending on the experimental parameters) indicate that such authentication is feasible and reveal clear guidelines towards future work.