ECG Authentication for Mobile Devices

Arteaga-Falconi, Juan; Osman, Hussein Al; Saddik, Abdulmotaleb El

doi:10.1109/tim.2015.2503863

Cited by 169 publications

(90 citation statements)

References 43 publications

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“…We have increased the accuracy rate and reduced the false accept rate significantly, comparing with previoues related work like in [1,6] cases. By reducing the false accepted rate, the authentication accuracy will be steadily increased.…”

Section: Resultssupporting

confidence: 52%

“…The security level at a system is measured by the system immunity against intrusion and fake signature to identify the person during the authentication process [1]. Technically, some biometric methods have strong uniqueness such as scanning iris and retina.…”

Section: Introductionmentioning

confidence: 99%

“…There are several techniques for extracting ECG In the current biometric authentication using ECG signal waves related works, many researches had been performed; like in [1], the researchers have propused mobile biometric authentication algorithm based on ECG signal. They have conducted two experiment types; experiment using physionet ECG records and laboratory experiment.…”

Section: Introductionmentioning

confidence: 99%

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Utilizing ECG Waveform Features as New Biometric Authentication Method

Shdefat¹,

Joo²,

Kim³

2018

IJECE

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In this study, we are proposing a practical way for human identification based on a new biometric method. The new method is built on the use of the electrocardiogram (ECG) signal waveform features, which are produced from the process of acquiring electrical activities of the heart by using electrodes placed on the body. This process is launched over a period of time by using a recording device to read and store the ECG signal. On the contrary of other biometrics method like voice, fingerprint and iris scan, ECG signal cannot be copied or manipulated. The first operation for our system is to record a portion of 30 seconds out of whole ECG signal of a certain user in order to register it as user template in the system. Then the system will take 7 to 9 seconds in authenticating the template using template matching techniques. 44 subjects" raw ECG data were downloaded from Physionet website repository. We used a template matching technique for the authentication process and Linear SVM algorithm for the classification task. The accuracy rate was 97.2% for the authentication process and 98.6% for the classification task; with false acceptance rate 1.21%.

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Section: Resultssupporting

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Utilizing ECG Waveform Features as New Biometric Authentication Method

Shdefat¹,

Joo²,

Kim³

2018

IJECE

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“…Security issues for IoT systems or mobile devices have been addressed in many papers [22][23][24][25][26][27][28]. In particular, Mohamed et al [23] proposed a game-based model for adaptive security in the IoT, with an emphasis on eHealth applications.…”

Section: Related Studiesmentioning

confidence: 99%

“…Authentication is to identify users to enable services for legitimate users only, and is considered one of the key aspects of security issues. Biometric authentication [25][26][27][28] has been the most popular method for individual identification. Biometric authentication involves the comparison of an input data with stored biometric data to identify a specific user.…”

Section: Related Studiesmentioning

confidence: 99%

An IoT System for Remote Monitoring of Patients at Home

Park

Lee

2017

Applied Sciences

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Application areas that utilize the concept of IoT can be broadened to healthcare or remote monitoring areas. In this paper, a remote monitoring system for patients at home in IoT environments is proposed, constructed, and evaluated through several experiments. To make it operable in IoT environments, a protocol conversion scheme between ISO/IEEE 11073 protocol and oneM2M protocol, and a Multiclass Q-learning scheduling algorithm based on the urgency of biomedical data delivery to medical staff are proposed. In addition, for the sake of patients' privacy, two security schemes are proposed-the separate storage scheme of data in parts and the Buddy-ACK authorization scheme. The experiment on the constructed system showed that the system worked well and the Multiclass Q-learning scheduling algorithm performs better than the Multiclass Based Dynamic Priority scheduling algorithm. We also found that the throughputs of the Multiclass Q-learning scheduling algorithm increase almost linearly as the measurement time increases, whereas the throughputs of the Multiclass Based Dynamic Priority algorithm increase with decreases in the increasing ratio.

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A physiological data‐driven model for learners' cognitive load detection using HRV‐PRV feature fusion and optimized XGBoost classification

et al. 2019

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Due to the increasing attention to online learning, cognitive load has been recently considered as a crucial indicator for judging teenagers' learning state so as to improve both learning and teaching effects. However, some traditional cognitive load measurement methods such as subjective measurement are easily influenced by subjective sensation deviation of subjects. None of them can reflect the cognitive load of learners more precisely. Recently, machine learning-based data modeling has gained more importance in the scenarios of various smart wearables and Internet of things applications. Meanwhile, physiological signals have proven to contribute much to human health assessment. On the basis of the above considerations, this paper proposes a physiological data-driven model for learners' cognitive load detection under the application of smart wearables. The model consists of four modules: physiological signal acquisition, signal preprocessing, heart rate variability and pulse rate variability feature fusion, and cognitive load classification through an optimized extreme gradient boosting classifier in which hyperparameters are adaptively tuned with sequential model-based optimization. Furthermore, we design an experimental paradigm for signal acquisition in a learning environment, and the experimental results demonstrate that the proposed model for cognitive load detection outperforms conventional approaches that only employ either heart rate variability or pulse rate variability for modeling. We also compare the effects of different feature fusion algorithms combined with different classification algorithms, which demonstrates that the proposed model achieves the highest accuracy of cognitive load detection due to its optimal combination of feature fusion and classification.

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ECG Authentication for Mobile Devices

Cited by 169 publications

References 43 publications

Utilizing ECG Waveform Features as New Biometric Authentication Method

Utilizing ECG Waveform Features as New Biometric Authentication Method

An IoT System for Remote Monitoring of Patients at Home

A physiological data‐driven model for learners' cognitive load detection using HRV‐PRV feature fusion and optimized XGBoost classification

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