Personal recognition using geometric features in the phase space of electrocardiogram

Kim, D. H.; Park, J. S.; Kim, I. Y.; Kim, S. I.; Lee, J. S.

doi:10.1109/lsc.2017.8268177

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…In ref. [20], the authors proposed a method using phase-space reconstruction (PSR) of a single lead of ECG. They used a time delay technique to reconstruct the ECG's signal into phase space to find the best identifiable time-delay value.…”

Section: (Heart-beat Print) Using Electrocardiogram (Ecg)mentioning

confidence: 99%

Your Vital Signs as Your Password?

Alrubaish¹,

Saqib²

2022

Recent Advances in Biometrics

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Cognitive biometrics (vital signs) indicate the individual’s authentication using his/her mental and emotional status specifically, electrocardiogram (ECG) and electroencephalogram (EEG). The motivation behind cognitive biometrics is their uniqueness, their absolute universality in each living individual, and their resistance toward spoofing and replaying attacks in addition to their indication of life. This chapter investigates the ability to use the vital sign as unimodal authentication in its status by surveying the recent techniques, their requirements and limitation, and whether it is ready to be used in the real market or not. Our observations state—that the vital signs can be considered as a PASSWORD due to their uniqueness, but it needs more improvements to be deployed to the market.

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Section: (Heart-beat Print) Using Electrocardiogram (Ecg)mentioning

confidence: 99%

Your Vital Signs as Your Password?

Alrubaish¹,

Saqib²

2022

Recent Advances in Biometrics

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“…Additionally, an Electrocardiogram (ECG) signal (that is, a sensor signal) is reconstructed into phase space using the time-delay technique. The 21 geometric features through the trajectory from the phase space are extracted, and the four daily activities (i.e., rest, exercise, listening to music, and watching a video) are recognized by the support vector machine learning, and the accuracy rate is 97.7% [19]. Based on the dynamic system and chaos theory, Md et al [3] proposed a human activity recognition system that is based on lightweight smartphone.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Chaotic Features-Based Human Activity Recognition

Cao

et al. 2021

Electronics

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Human activity recognition (HAR) has vital applications in human–computer interaction, somatosensory games, and motion monitoring, etc. On the basis of the human motion accelerate sensor data, through a nonlinear analysis of the human motion time series, a novel method for HAR that is based on non-linear chaotic features is proposed in this paper. First, the C-C method and G-P algorithm are used to, respectively, compute the optimal delay time and embedding dimension. Additionally, a Reconstructed Phase Space (RPS) is formed while using time-delay embedding for the human accelerometer motion sensor data. Subsequently, a two-dimensional chaotic feature matrix is constructed, where the chaotic feature is composed of the correlation dimension and largest Lyapunov exponent (LLE) of attractor trajectory in the RPS. Next, the classification algorithms are used in order to classify and recognize the two different activity classes, i.e., basic and transitional activities. The experimental results show that the chaotic feature has a higher accuracy than traditional time and frequency domain features.

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Personal recognition using geometric features in the phase space of electrocardiogram

Cited by 2 publications

References 8 publications

Your Vital Signs as Your Password?

Your Vital Signs as Your Password?

Non-Linear Chaotic Features-Based Human Activity Recognition

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