Activity‑aware electrocardiogram biometric verification utilising deep learning on wearable devices

Abstract

With the advancement of technology and the increasing use of wearable devices, information security have become a necessity. Although many biometrics authentication methods have been studied on these devices to ensure information security, an activity-aware deep learning (DL) model that is compatible with different device types and uses only electrocardiogram signals has not been studied. Our objective is to investigate DL models that exclusively use ECG signals during several physical activities, facilitating their implementation on various devices. Through this research, we aim to contribute to the advancement of wearable devices for the purpose of biometric verification. In this context, this study investigates the application of adaptive techniques that rely on prior activity classification to potentially improve biometric performance using DL models. In this study, we compare three time-frequency representations to generate images for activity classification using GoogleNet, ResNet50 and DenseNet201, and for biometric verification using ResNet50 and DenseNet201. Despite employing various convolutional neural network (CNN) models, we could not achieve high accuracy in activity classification. Consequently, manually classified samples were used for activity-aware biometric verification. We also provide a detailed comparison of various DL parameters. We use a public dataset simultaneously collected from both medical and wearable devices to offer a cross-device comparison. The results demonstrate that our method can be applied to both wearable and medical devices for activity classification and biometric verification. Besides, although it is known that DL requires a large amount of training data, our model, which was created using a small amount of training data and a real-life biometric verification scenario, achieved comparable results to studies using a large amount of data. The model was achieved 0.16% to 30.48% better results when classified according to their physical activities.

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