Over the last years, there has been an increasing research interest in the application of medical biometrics, which may be applied and implemented in new automated recognition systems. The need for more security and health monitoring is increasing with new functionalities and features made available. To improve different device/application security and health monitoring we present a wireless biometric electrocardiography (ECG) system using both machine learning approaches and stable template creation/comparison algorithms. Unlike other previous work on wearable ECG recognition, which were based on full wired non-wireless systems, this paper reports a new wireless technology and techniques which can improve the performance, by using simple and low-cost components. Several techniques were applied to our 30 volunteer chest-based ECG dataset that resulted in up to 97.5% identification accuracy rate and an Equal Error Rate (EER) of 0.98%.
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- Wireless Chest-Based ECG Biometrics
- Springer Berlin Heidelberg