使用可穿戴设备进行基于活动的心电图生物特征验证

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
IET Biometrics Pub Date : 2022-12-16 DOI:10.1049/bme2.12105
Hazal Su Bıçakcı, Marco Santopietro, Richard Guest
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引用次数: 1

摘要

活动分类和生物识别认证已成为智能手表和追踪器等可穿戴技术的代名词。尽管已经做出了巨大的努力来开发基于心电图(ECG)的生物特征验证和识别模式,使用来自这些设备的数据,但在本文中,我们探索了使用基于先验活动分类的自适应技术,试图提高生物特征性能。在这样做的过程中,我们还比较了两个波形相似性距离,以提供用于分类的特征。从医疗和可穿戴设备收集的两个公共数据集提供了跨设备比较。我们的结果表明,我们的方法能够用于活动分类和生物特征验证案例中的可穿戴设备和医疗设备。这项研究是第一项仅使用心电图信号进行活动分类和生物特征验证的研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Activity-based electrocardiogram biometric verification using wearable devices

Activity-based electrocardiogram biometric verification using wearable devices

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.

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来源期刊
IET Biometrics
IET Biometrics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
5.90
自引率
0.00%
发文量
46
审稿时长
33 weeks
期刊介绍: The field of biometric recognition - automated recognition of individuals based on their behavioural and biological characteristics - has now reached a level of maturity where viable practical applications are both possible and increasingly available. The biometrics field is characterised especially by its interdisciplinarity since, while focused primarily around a strong technological base, effective system design and implementation often requires a broad range of skills encompassing, for example, human factors, data security and database technologies, psychological and physiological awareness, and so on. Also, the technology focus itself embraces diversity, since the engineering of effective biometric systems requires integration of image analysis, pattern recognition, sensor technology, database engineering, security design and many other strands of understanding. The scope of the journal is intentionally relatively wide. While focusing on core technological issues, it is recognised that these may be inherently diverse and in many cases may cross traditional disciplinary boundaries. The scope of the journal will therefore include any topics where it can be shown that a paper can increase our understanding of biometric systems, signal future developments and applications for biometrics, or promote greater practical uptake for relevant technologies: Development and enhancement of individual biometric modalities including the established and traditional modalities (e.g. face, fingerprint, iris, signature and handwriting recognition) and also newer or emerging modalities (gait, ear-shape, neurological patterns, etc.) Multibiometrics, theoretical and practical issues, implementation of practical systems, multiclassifier and multimodal approaches Soft biometrics and information fusion for identification, verification and trait prediction Human factors and the human-computer interface issues for biometric systems, exception handling strategies Template construction and template management, ageing factors and their impact on biometric systems Usability and user-oriented design, psychological and physiological principles and system integration Sensors and sensor technologies for biometric processing Database technologies to support biometric systems Implementation of biometric systems, security engineering implications, smartcard and associated technologies in implementation, implementation platforms, system design and performance evaluation Trust and privacy issues, security of biometric systems and supporting technological solutions, biometric template protection Biometric cryptosystems, security and biometrics-linked encryption Links with forensic processing and cross-disciplinary commonalities Core underpinning technologies (e.g. image analysis, pattern recognition, computer vision, signal processing, etc.), where the specific relevance to biometric processing can be demonstrated Applications and application-led considerations Position papers on technology or on the industrial context of biometric system development Adoption and promotion of standards in biometrics, improving technology acceptance, deployment and interoperability, avoiding cross-cultural and cross-sector restrictions Relevant ethical and social issues
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