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Sensor Technologies and Intelligent Computing for Biometric Signal Analysis and Pattern Recognition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 4493

Special Issue Editors

Dr. Min Wang

E-Mail Website
Guest Editor
School of Systems and Computing, University of New South Wales, Canberra, ACT 2612, Australia
Interests: biometrics; pattern recognition; deep learning; privacy and security
Special Issues, Collections and Topics in MDPI journals
Dr. Raul Fernandez Rojas

E-Mail Website
Guest Editor
School of Information Technology and Systems, University of Canberra, Canberra, ACT 2617, Australia
Interests: neurophysiological sensors; EEG; fNIRS; ECG; signal processing; cognitive computing
Special Issues, Collections and Topics in MDPI journals
Dr. Essam Debie

E-Mail Website
Guest Editor
School of Systems and Computing, University of New South Wales, Canberra, ACT 2612, Australia
Interests: data mining; machine learning; cognitive computing; trusted autonomy

Special Issue Information

Dear Colleagues,

This Special Issue aims to compile cutting-edge research in the interdisciplinary domains of sensor technologies, intelligent computing, and biometric signal analysis, with a specific focus on the advancement of pattern recognition methodologies. The proliferation of sensor technologies has empowered the acquisition of diverse and intricate biometric signals, opening avenues for innovative approaches to pattern recognition using intelligent computing techniques.

We invite contributions from researchers and practitioners in fields such as computer science, electrical engineering, biomedical engineering, and artificial intelligence, among others. Topics of interest include, but are not limited to, novel sensor designs, advanced signal processing algorithms, machine learning applications, and the integration of intelligent computing paradigms for biometric signal analysis and pattern recognition.

Topics of interest include, but are not limited to, the following methods:

  • Sensor and sensing technologies for biometrics;
  • Face, fingerprint, hand, iris, brain, and other emerging biometrics;
  • Behavioral and physiological signal-based biometrics;
  • Multimodal biometrics;
  • IoT and wearable Sensors;
  • Biometric data/signal analysis;
  • Computational methods in feature extraction and recognition;
  • Machine learning for biometrics;
  • Deepfake and Anti-Deepfake methods;
  • Privacy and security.

Dr. Min Wang
Dr. Raul Fernandez Rojas
Dr. Essam Debie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multimodal biometrics
  • wearable sensors
  • biometric data/signal analysis
  • privacy and security

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

29 pages, 3854 KiB  
Article
Automated ECG Arrhythmia Classification Using Feature Images with Common Matrix Approach-Based Classifier
by Ali Kirkbas and Aydin Kizilkaya
Sensors 2025, 25(4), 1220; https://doi.org/10.3390/s25041220 - 17 Feb 2025
Cited by 1 | Viewed by 899
Abstract
This paper seeks to solve the classification problem of cardiac arrhythmias by using a small number of electrocardiogram (ECG) recordings. To offer a reasonable solution to this problem, a technique that combines a common matrix approach (CMA)-based classifier model with the Fourier decomposition [...] Read more.
This paper seeks to solve the classification problem of cardiac arrhythmias by using a small number of electrocardiogram (ECG) recordings. To offer a reasonable solution to this problem, a technique that combines a common matrix approach (CMA)-based classifier model with the Fourier decomposition method (FDM) is proposed. The FDM is responsible for generating time–frequency (T-F) representations of ECG recordings. The classification process is performed with feature images applied as input to the classifier model. The feature images are obtained after two-dimensional principal component analysis (2DPCA) of data matrices related to ECG recordings. Each data matrix is created by concatenating the ECG record itself, the Fourier transform, and the T-F representation on a single matrix. To verify the efficacy of the proposed method, various experiments are conducted with the MIT-BIH, Chapman, and PTB-XL databases. In the assessments using the MIT-BIH database under the inter-patient paradigm, we achieved a mean overall accuracy rate of 99.81%. The proposed method outperforms the majority of recent efforts, yielding rates exceeding 99% on nearly five performance metrics for the recognition of V- and S-class arrhythmias. It is found that, in the classification of four types of arrhythmias using ECG recordings from the Chapman database, our model surpasses recent works by reaching mean overall accuracy rates of 99.76% and 99.45% for the raw and de-noised ECG recordings, respectively. Similarly, five different forms of arrhythmias from the PTB-XL database were recognized with a mean overall accuracy of 98.71%. Full article
(This article belongs to the Special Issue Sensor Technologies and Intelligent Computing for Biometric Signal Analysis and Pattern Recognition)
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15 pages, 556 KiB  
Article
Lightweight Hash-Based Authentication Protocol for Smart Grids
by Sangjin Kook, Keunok Kim, Jihyeon Ryu, Youngsook Lee and Dongho Won
Sensors 2024, 24(10), 3085; https://doi.org/10.3390/s24103085 - 13 May 2024
Cited by 3 | Viewed by 1446
Abstract
Smart grids integrate information and communications technology into the processes of electricity production, transportation, and consumption, thereby enabling interactions between power suppliers and consumers to increase the efficiency of the power grid. To achieve this, smart meters (SMs) are installed in households or [...] Read more.
Smart grids integrate information and communications technology into the processes of electricity production, transportation, and consumption, thereby enabling interactions between power suppliers and consumers to increase the efficiency of the power grid. To achieve this, smart meters (SMs) are installed in households or buildings to measure electricity usage and allow power suppliers or consumers to monitor and manage it in real time. However, SMs require a secure service to address malicious attacks during memory protection and communication processes and a lightweight communication protocol suitable for devices with computational and communication constraints. This paper proposes an authentication protocol based on a one-way hash function to address these issues. This protocol includes message authentication functions to address message tampering and uses a changing encryption key for secure communication during each transmission. The security and performance analysis of this protocol shows that it can address existing attacks and provides 105,281.67% better computational efficiency than previous methods. Full article
(This article belongs to the Special Issue Sensor Technologies and Intelligent Computing for Biometric Signal Analysis and Pattern Recognition)
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16 pages, 2270 KiB  
Article
Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features
by Ho Bin Hwang, Jeyeon Lee, Hyeokchan Kwon, Byungho Chung, Jongshill Lee and In Young Kim
Sensors 2024, 24(5), 1556; https://doi.org/10.3390/s24051556 - 28 Feb 2024
Cited by 3 | Viewed by 1331
Abstract
In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses [...] Read more.
In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses practical challenges due to their need for flexibility and convenience. Electrocardiograms (ECGs) have emerged as a potential solution to these issues but face hurdles due to intra-individual variability. This study aims to evaluate the possibility of a stable, flexible, and convenient-to-use bio-key using ECGs. We propose an approach that minimizes biosignal variability using normalization, clustering-based binarization, and the fuzzy extractor, enabling the generation of personalized seeds and offering ease of use. The proposed method achieved a maximum entropy of 0.99 and an authentication accuracy of 95%. This study evaluated various parameter combinations for generating effective bio-keys for personal authentication and proposed the optimal combination. Our research holds potential for security technologies applicable to wearable devices and healthcare systems. Full article
(This article belongs to the Special Issue Sensor Technologies and Intelligent Computing for Biometric Signal Analysis and Pattern Recognition)
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