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.
ORIGINAL RESEARCH, activity classification, behavioural biometrics, biometrics (access control), ECG biometrics, emotion recognition, wearable devices
38-51
Bıçakcı, Hazal Su
d834ab27-440f-4cff-ad6f-1bd84117653e
Santopietro, Marco
fcfe5a84-a740-4a15-898c-a170b48a8264
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Bıçakcı, Hazal Su
d834ab27-440f-4cff-ad6f-1bd84117653e
Santopietro, Marco
fcfe5a84-a740-4a15-898c-a170b48a8264
Guest, Richard
93533dbd-b101-491b-83cc-39ccfdc18165
Bıçakcı, Hazal Su, Santopietro, Marco and Guest, Richard
(2022)
Activity based electrocardiogram biometric verification using wearable devices.
IET Biometrics, 12 (1), .
(doi:10.1049/bme2.12105).
Abstract
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.
Text
IET Biometrics - 2022 - Bıçakcı - Activity‐based electrocardiogram biometric verification using wearable devices
- Version of Record
More information
Accepted/In Press date: 6 December 2022
e-pub ahead of print date: 16 December 2022
Keywords:
ORIGINAL RESEARCH, activity classification, behavioural biometrics, biometrics (access control), ECG biometrics, emotion recognition, wearable devices
Identifiers
Local EPrints ID: 489506
URI: http://eprints.soton.ac.uk/id/eprint/489506
ISSN: 2047-4938
PURE UUID: 36667e56-22ca-432b-a958-d6b390e6521d
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Date deposited: 25 Apr 2024 16:36
Last modified: 28 Apr 2024 02:05
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Contributors
Author:
Hazal Su Bıçakcı
Author:
Marco Santopietro
Author:
Richard Guest
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