Biometric authentication is nowadays widely used in a multitude of scenarios. Several studies have been conducted on electrocardiogram (ECG) for subject identification or verification among the various modalities. However, none have considered a typical implementation with a mobile device and the necessity for a fast-training model with limited recording time for the signal. This study tackles this issue by exploring various classification models on short recordings and evaluating the performance varying the sample length and the training set size. We run our tests on two public datasets collected from wearable and medical devices and propose a pipeline for ECG authentication with limited data required for competitive usage across applications.

Activity classification and biometric authentication have become synonymous with wearable technologies such as smartwatches and trackers. Although great efforts have been made to develop electrocardiogram (ECG)-based biometric verification and identification modalities using data from these devices, in this paper, we explore the use of adaptive techniques based on prior activity classification in an attempt to enhance biometric performance. In doing so, we also compare two waveform similarity distances to provide features for classification. Two public datasets which were collected from medical and wearable devices provide a cross-device comparison. Our results show that our method is able to be used for both wearable and medical devices in activity classification and biometric verification cases. This study is the first study which uses only ECG signals for both activity classification and biometric verification purposes. K E Y W O R D S activity classification, behavioural biometrics, biometrics (access control), ECG biometrics, emotion recognition, wearable devicesThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.