Exploring Seismocardiogram Biometrics with Wavelet Transform

Hsu, Po‐Ya; Hsu, Po Kuei; Liu, Hsin‐Li

doi:10.1109/icpr48806.2021.9412582

Cited by 4 publications

(14 citation statements)

References 21 publications

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“…Therefore, the coefficients of DWT are usually directly extracted into a feature vector for classification with conventional classifiers such as support vector machines, hidden Markov models, and discriminant analysis [ 83 , 87 ]. In contrast, STFT and CWT can be used to create two-dimensional time–frequency representations (often referred to as spectrograms and scalograms, respectively), which are then fed into convolution neural networks [ 76 , 77 , 88 , 89 ]. Wavelet packet decomposition (WPD) generalizes the DWT in preserving the detail coefficients, which capture the information lost between two successive approximation coefficients in each filtering step.…”

Section: Representation Learning In Cognitive Biometricsmentioning

confidence: 99%

“…SVMs are even more popular than DA-based methods in cognitive biometric recognition. They have been used in a wide range of classification tasks for EEG [ 56 , 67 , 71 , 83 ], ECG [ 37 , 66 , 154 ], SCG [ 89 ] and EDA signals [ 43 ]. In these studies, the SVMs are equipped with linear kernels [ 48 , 83 , 155 ] and non-linear kernels including the radial basis function [ 48 , 67 , 69 , 71 ], sigmoid function, and polynomial function [ 48 ].…”

Section: Pattern Recognition In Cognitive Biometricsmentioning

confidence: 99%

“…Other studies have applied the hidden Markov model (HMM), GMM, random forests (RFs), and other methods to cognitive biometric recognition based on ECG [ 38 , 158 , 160 ], PPG [ 161 ], SCG [ 89 , 162 ], PCG [ 79 , 163 , 164 ], EEG [ 52 , 83 , 165 ], and EDA signals [ 77 ]. Among them, HMMs, which are able to model raw sequential data and detect non-stationary changes, have been demonstrated to be efficient algorithms for classifying cognitive biological signals.…”

Section: Pattern Recognition In Cognitive Biometricsmentioning

confidence: 99%

“…In addition, benefiting from the usage of convolution kernels, CNN networks can be used to effectively extract spatial relationship patterns/features from the input signals. CNNs have been used in various cognitive signal recognition such as EEG [ 17 , 98 , 179 , 180 ], ECG [ 88 , 181 , 182 , 183 ], PPG [ 184 ], SCG [ 80 , 89 ], and EDA [ 43 , 77 ]. The CNN-based methods proposed in [ 26 , 179 , 180 , 185 ] take as input 2D timeseries EEG signals to model spatial–temporal information.…”

Section: Pattern Recognition In Cognitive Biometricsmentioning

confidence: 99%

“…Everson et al [ 184 ] proposed using a CNN network to preliminarily extract spatial information from 2D PPG signals. Maiorana et al [ 80 ] and Hsu et al [ 89 ] presented CNN networks for SCG-based recognition to learn temporal–spectral information from 2D STFT coefficients and 2D CWT coefficients, respectively. A CNN network was utilized for EDA-based recognition based on 1D handcrafted features [ 43 ], while Piciucco et al [ 77 ] proposed a CNN-based recognition method by modeling temporal–spectral information from 2D STFT coefficients of EDA signals.…”

Section: Pattern Recognition In Cognitive Biometricsmentioning

confidence: 99%

See 4 more Smart Citations

Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey

Wang

Yin

Zhu

et al. 2022

Sensors

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Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.

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Section: Representation Learning In Cognitive Biometricsmentioning

confidence: 99%

Section: Pattern Recognition In Cognitive Biometricsmentioning

confidence: 99%

Section: Pattern Recognition In Cognitive Biometricsmentioning

confidence: 99%

Section: Pattern Recognition In Cognitive Biometricsmentioning

confidence: 99%

Section: Pattern Recognition In Cognitive Biometricsmentioning

confidence: 99%

See 3 more Smart Citations

Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey

Wang

Yin

Zhu

et al. 2022

Sensors

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EEG Signal Processing to Control a Finger Rehabilitation System

FallahTaherpazir,

Menhaj,

Sajedin

2023

Preprint

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This study aims to provide a comprehensive comparison for classification of Electroencephalography (EEG) signal based motor imagery, in time domain and time-frequency domain with different classifiers. We used EEG signals recorded while the subjects were imagining the movement of individual fingers, and analyzed the signals in time domain as well as using wavelet transform and Wigner transform. Our main goal is to compare different methods of feature extraction and classification as the important steps in the process of EEG signals for the Brain-Computer Interface (BCI) system. The experimental results indicate that the Support Vector Machine (SVM) method provides a better classification performance compared with other classification methods. Also, Linear Discriminative Analysis (LDA) performs as well as the SVM, after applying PCA for dimension reduction. The proposed scheme can be applied successfully to BCI systems where the amount of large data.

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BIOWISH: Biometric Recognition Using Wearable Inertial Sensors Detecting Heart Activity

Maiorana

Romano

Schena

et al. 2024

IEEE Trans. Dependable and Secure Comput.

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Wearable devices have been recently proposed to perform biometric recognition, leveraging on the uniqueness of the collectable physiological traits to generate discriminative identifiers. Most of the studies conducted on this topic have exploited heart-related signals, sensing the cardiac activity either through electrical measurements using electrocardiography, or with optical recordings employing photoplethysmography. In this paper we instead propose a system performing BIOmetric recognition using Wearable Inertial Sensors detecting Heart activity (BIOWISH). In more detail, we investigate the feasibility of exploiting mechanical measurements obtained through seismocardiography and gyrocardiography to verify the identity of a subject. Several feature extractors and classifiers, including deep learning techniques relying on siamese training, are employed to derive distinctive characteristics from the considered signals, so as to differentiate between legitimate users and impostors. A multi-session database, comprising acquisitions taken from subjects performing different activities, is employed to perform experimental tests. The obtained results testify that identifiers derived from measurements of chest vibrations, collected by wearable inertial sensors, could be employed to guarantee high recognition performance, even when considering short-time recordings. Explainability methods have been also employed to derive some insights about the aspects relevant to perform predictions for both people and activity recognition tasks.

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Exploring Seismocardiogram Biometrics with Wavelet Transform

Cited by 4 publications

References 21 publications

Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey

Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey

EEG Signal Processing to Control a Finger Rehabilitation System

BIOWISH: Biometric Recognition Using Wearable Inertial Sensors Detecting Heart Activity

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