Biometric Data Research Articles

Brief Review and Primer of Key Terminology for Artificial Intelligence and Machine Learning in Hypertension.

Recent breakthroughs in artificial intelligence (AI) have caught the attention of many fields, including health care. The vision for AI is that a computer model can process information and provide output that is indistinguishable from that of a human and, in specific repetitive tasks, outperform a human's capability. The 2 critical underlying technologies in AI are used for supervised and unsupervised machine learning. Machine learning uses neural networks and deep learning modeled after the human brain from structured or unstructured data sets to learn, make decisions, and continuously improve the model. Natural language processing, used for supervised learning, is understanding, interpreting, and generating information using human language in chatbots and generative and conversational AI. These breakthroughs result from increased computing power and access to large data sets, setting the stage for releasing large language models, such as ChatGPT and others, and new imaging models using computer vision. Hypertension management involves using blood pressure and other biometric data from connected devices and generative AI to communicate with patients and health care professionals. AI can potentially improve hypertension diagnosis and treatment through remote patient monitoring and digital therapeutics.

Cognitive and behavioral markers for human detection error in AI-assisted bridge inspection

Integrating Artificial Intelligence (AI) and drone technology into bridge inspections offers numerous advantages, including increased efficiency and enhanced safety. However, it is essential to recognize that this integration changes the cognitive ergonomics of the inspection task. Gaining a deeper understanding of how humans process information and behave when collaborating with drones and AI systems is necessary for designing and implementing effective AI-assisted inspection drones. To further understand human-drone-AI intricate dynamics, an experiment was conducted in which participants’ biometric and behavioral data were collected during a simulated drone-enabled bridge inspection under two conditions: with an 80% accurate AI assistance and with no AI assistance. Results indicate that cognitive and behavioral factors, including vigilance, cognitive processing intensity, gaze patterns, and visual scanning efficiency can influence inspectors' performance respectively in either condition. This highlights the importance of designing inspection protocols, drones and AI systems based on a comprehensive understanding of the cognitive processes required in each condition to prevent cognitive overload and minimize errors. We also remark on the visual scanning and gaze patterns associated with a higher chance of missing critical information in each condition, insights that inspectors can use to enhance their inspection performance.

‘They must know their rights’– reflecting on privacy, informed consent and the digital agency of asylum seekers and refugees in border contexts

AbstractThe article questions digital agency as a subjective experience of refugees when crossing Europe's external and internal borders. More concretely, the article asks how refugees experience digital agency, and how this concrete experience is constituted in specific border practices. In doing so, it examines the contradictions that arise between European laws and human rights in the context of border practices. The analysis of the data of this paper reflects two important components of digital agency: the sense of ownership and control of one's body and actions, and the capacity to think independently and thus make considered choices. This study is based on a qualitative research approach based on narrative interviews. The data was imported to MAXQDA, a software package that allows data to be efficiently collected, organised, analysed and visualised electronically. The article shows how refugees and asylum seekers, when crossing the border to Europe, do not simply enter another country, but a (powerful) institutional system. In having their biometric and digital data collected, they are confronted with several practices of border and risk management that have become routinised and thus ‘normalised’ on an institutional level. Thus, their digital agency must be understood as precarious, underscored by an installed coercive environment.

Smart city energy efficient data privacy preservation protocol based on biometrics and fuzzy commitment scheme

Advancements in cloud computing, flying ad-hoc networks, wireless sensor networks, artificial intelligence, big data, 5th generation mobile network and internet of things have led to the development of smart cities. Owing to their massive interconnectedness, high volumes of data are collected and exchanged over the public internet. Therefore, the exchanged messages are susceptible to numerous security and privacy threats across these open public channels. Although many security techniques have been designed to address this issue, most of them are still vulnerable to attacks while some deploy computationally extensive cryptographic operations such as bilinear pairings and blockchain. In this paper, we leverage on biometrics, error correction codes and fuzzy commitment schemes to develop a secure and energy efficient authentication scheme for the smart cities. This is informed by the fact that biometric data is cumbersome to reproduce and hence attacks such as side-channeling are thwarted. We formally analyze the security of our protocol using the Burrows–Abadi–Needham logic logic, which shows that our scheme achieves strong mutual authentication among the communicating entities. The semantic analysis of our protocol shows that it mitigates attacks such as de-synchronization, eavesdropping, session hijacking, forgery and side-channeling. In addition, its formal security analysis demonstrates that it is secure under the Canetti and Krawczyk attack model. In terms of performance, our scheme is shown to reduce the computation overheads by 20.7% and hence is the most efficient among the state-of-the-art protocols.

Protection of biometric private life under the European Convention on Human Rights and the law of armed conflict

By definition, biometrics consists of sensitive data about the individual. Therefore, the State must ensure a higher degree of safeguarding measures in all phases of handling such data. This article argues that data-subjects whose biometrics are gathered for identification are protected through the right to biometric private life under international human rights law at all times. Furthermore, the law of armed conflict also contains relevant provisions. The European Convention on Human Rights provides detailed rules for the protection of biometric private life, subject to limitations and possibly derogations. This protection covers the whole data life cycle of collection, retention and disclosure of biometrics. Two points are of importance here when assessing what is permissible: (1) The original purpose for which biometric data collection was initiated remains decisive. (2) The use of special measures such as limitations or derogations, does not permit States to annihilate the protection of a person’s biometric private life. The right to biometric private life remains protected during armed conflict, because international human rights law and the law of armed conflict re-enforce each other. Therefore, armed forces must: (1) define in detail for what purpose biometric data is collected, retained and disclosed, and (2) maintain strict adherence to the originally intended usage of the biometric data and refrain from any divergence therefrom. This article argues that the legal framework under international human rights law in general and the European Convention on Human Rights in particular, is sufficiently flexible to balance the interests of the rights holder with those of the State or the community at large. Nevertheless, this flexibility makes it harder to predict the outcomes of a legal assessment, and a case-by-case approach must therefore be expected.

From Russian filtration operations to the lex ferenda of belligerent occupation: how to protect biometrics and privacy in occupied territories?

This article begins by analysing Russian filtration operations and the collection and use of Ukrainian inhabitants’ biometric information. It finds that whereas the Russian filtration operations constitute a clear violation of the law of belligerent occupation, the collection of personal and biometric information is unlikely to constitute a separate and independent violation. This article argues that the current law of belligerent occupation is unsatisfactory and fails to provide sufficient protection for inhabitants. It compares six different ways to further develop the laws of belligerent occupation to enhance the protection of inhabitants’ biometric data and privacy interests. This article concludes that the best approach is to recognize biometric data as private property under the lex ferenda of belligerent occupation.

Biometrics and new and emerging technologies – future challenges in the use of biometric data in the conduct of military operations

The collection of biometric information is subject to domestic, as well as international legal regulation. These regimes can pose complex challenges to military forces seeking to use biometric data during military operations. Using Australia as a case study, this article identifies how the Australian domestic legal regime applies to the collection, disclosure and use of biometric data by the Australian Defence Force (ADF) when conducting military operations outside Australia. It analyses the impact of this legal regime on the ADF’s participation in coalition operations, deriving a range of principles relevant to the exchange of biometric data that can aid interoperability during coalition operations. It highlights the value of conscious planning for the use of biometric data, detailed knowledge of coalition partner domestic law requirements and information sharing agreements to the conduct of legally robust coalition military operations.

Catheter-assisted local lysis therapy for submassive pulmonary embolism

Pulmonary embolism is the third most common cardiovascular disease. Interventional treatment options as an alternative to systemic lysis therapy of hemodynamically stable, submassive pulmonary embolisms have received an unprecedented boost in innovation in recent years. The treatment options are heterogeneous and can be roughly divided into local thrombolysis and local thrombectomy. For years in our center we have been carrying out catheter-assisted, locoregional lysis therapy with side-hole lysis catheters and acumulative dose per pulmonary branch of 10 mg alteplase over 15 h for hemodynamically stable, submassive pulmonary emboli. The aim of this retrospective study was to review this therapeutic concept and to collect data on clinical endpoints and possible complications. The study included data from 01/2018-03/2023. For this purpose, the patients were selected based on the OPS codes (8.838.60 and 1‑276.0), and the data was collected using the medical records. Biometric data, data on previous illnesses and vital parameters, laboratory chemistry data, CT diagnostic data, echocardiographic data, data on drug treatment and data on complications were collected anonymously. There was asignificant reduction in the strain on the right heart. Peripheral oxygen saturation also improved significantly and heart rate decreased significantly. The complication rate remained low and was almost exclusively limited to access-related problems. Catheter-assisted, locoregional lysis therapy is asafe and effective treatment method for submassive pulmonary embolism.

Biometric Security Systems Enhanced by AI: Exploring Concerns with AI Advancements in Facial Recognition and Other Biometric Systems have Security Implications and Vulnerabilities

A new age of accuracy and efficiency, especially in face recognition and other biometric technologies, has been brought about in recent years by the integration of artificial intelligence (AI) into biometric security systems. The discussion extends to the security implications of AI-enhanced biometric systems, including their susceptibility to threats such as spoofing and adversarial attacks. We analyze the vulnerabilities these systems face and propose advanced algorithmic solutions to fortify them against such risks. Moreover, this paper addresses the ethical and privacy concerns surrounding the widespread use of biometric data, emphasizing the need for stringent data protection measures and regulatory compliance. Additionally, the research investigates AI's significant contributions to genetic engineering, particularly through advancements in CRISPR [1] technology. By integrating AI, the precision of gene editing can be significantly improved, potentially revolutionizing personalized medicine and genetic therapies. This extensive research intends to shed light on the revolutionary potential of artificial intelligence (AI) in genetic engineering and biometric security, emphasizing both the exciting developments and the difficult obstacles still to be overcome. Through this research, readers will get a clearer knowledge of how artificial intelligence (AI) is altering biotechnology and security, opening the door for discoveries that might have a significant influence on healthcare and other fields.

The Role of Machine Learning in Advanced Biometric Systems

Today, the significance of biometrics is more pronounced than ever in accurately allowing access to valuable resources, from personal devices to highly sensitive buildings, as well as classified information. Researchers are pushing forward toward devising robust biometric systems with higher accuracy, fewer false positives and false negatives, and better performance. On the other hand, machine learning (ML) has been shown to play a key role in improving such systems. By constantly learning and adapting to users’ changing biometric patterns, ML algorithms can improve accuracy and performance over time. The integration of ML algorithms with biometrics, however, introduces vulnerabilities in such systems. This article investigates the new issues of concern that come about because of the adoption of ML methods in biometric systems. Specifically, techniques to breach biometric systems, namely, data poisoning, model inversion, bias injection, and deepfakes, are discussed. Here, the methodology consisted of conducting a detailed review of the literature in which ML techniques have been adopted in biometrics. In this study, we included all works that have successfully applied ML and reported favorable results after this adoption. These articles not only reported improved numerical results but also provided sound technical justification for this improvement. There were many isolated, unsupported, and unjustified works about the major advantages of ML techniques in improving security, which were excluded from this review. Though briefly mentioned, we did not touch upon encryption/decryption aspects, and, accordingly, cybersecurity was excluded from this study. At the end, recommendations are made to build stronger and more secure systems that benefit from ML adoption while closing the door to adversarial attacks.

Enhancing Iris Authentication for Managing Multiple Cancellations: Leveraging Quotient Filters

Biometric authentication methods have become increasingly popular for their ability to offer secure and convenient access control. However, concerns about the privacy and security of biometric data have arisen. In this study, we present a novel approach to address these concerns by proposing a cancellable biometric encryption technique for secure and format-preserving iris authentication. Our method leverages the Quotient Filter data structure to transform encrypted iris templates into cancellable templates while preserving their original format. We carefully select an appropriate format-preserving encryption algorithm for iris templates and design a mapping scheme to achieve cancellability. To assess the effectiveness and performance of our approach, extensive experiments are conducted. The quantitative results indicate the efficiency and efficacy of our cancellable biometric encryption technique using the Quotient Filter. Our innovation, the Iris Authentication for Multiple Cancelled Instances Using a Quotient Filter (IAMCIQF), demonstrates competitive performance across several key metrics. IAMCIQF achieves a high level of security strength and strikes a balance between security and efficiency in terms of key size, encryption and decryption speeds and storage efficiency when compared to other existing techniques. The quantitative outcomes underscore IAMCIQF’s potential as a promising solution for attaining secure and format-preserving iris authentication, addressing critical concerns about biometric data security.

Data-driven message optimization in dynamic sports media: an artificial intelligence approach to predict consumer response

ABSTRACT Artificial intelligence (AI) and big data have the potential to promote advancement across various industries. Sport management and marketing have also significantly transformed due to rapid technological advances such as those in AI and big data analytics. Especially sports companies, however, are still underutilizing the potential of AI. At the same time, considering the existing sport marketing research, the effectiveness and optimization of dynamic marketing stimuli in dynamic sport media settings remains unclear. This study aims to assess the differences between two AI models’ predictive capabilities with and without access to consumers’ biometric data when forecasting the influence of game features on consumers’ responses. Academic theoretical models indicate that individual biometric features have a considerable influence on consumers’ responses; nevertheless, it remains impractical for companies to access these data concerning message effectiveness and ROI evaluation. Therefore, the study attempts to enhance the feasibility of message optimization for companies by trialing a real-time prediction derived from game features alone, exemplifying how much predictive capability is lost by non-available consumer data. Two supervised machine learning models (one initial, primarily theoretical based model; one adapted model due to available data) were trained to reanalyze large-scale eye tracking and game-related data, resulting in high predictive accuracy and appropriate applicability of the models. Both models were able to predict consumers’ responses with over 90% accuracy (initial model: 96%; adapted model: 94%). This study exemplifies AI usage in sport marketing and management, enabling companies to implement more effective marketing messages and strategies for their sponsorship based on real-time evaluation.

Remote Perioperative Monitoring in Adult Cardiac Surgery: The Impact on 1000 Consecutive Patients

BackgroundOur remote patient monitoring (RPM) program for adult cardiac surgery patients aims to remove barriers to access, provide continuity of expert care, and increase their time-at-home. The RPM program integrates novel biosensors, an application for audiovisual visits, messaging, biometric data tracking, patient-reported outcomes, and scheduling with the aim of reducing postoperative length of stay and 30-day readmissions, while simultaneously increasing the rate of patients discharged to home. MethodsOur institutional database was utilized for this retrospective review of 1000 consecutive RPM patients who underwent coronary artery bypass, valve, and coronary artery bypass + valve, at 3 hospitals from July 2019 through April 2023. The study cohort was compared with 1000 propensity-matched controls from the same three hospitals (1:1, nearest neighbor matching where propensity scores were generated with RPM as the outcome measure). Patient characteristics, procedures, and outcomes are defined as per The Society of Thoracic Surgeons Adult Cardiac Database. ResultsRPM patients experienced statistically significant shorter median postoperative length of stay (1 day less, a 16.7% relative difference; P < .0001) and a 33% relative reduction in 30-day readmission (7.0 ± 0.8 vs 4.7 ± 0.7, P = .027), while 5.6% more patients were discharged to home (97.8% vs 92.2%, P < .0001) when compared with the non-RPM cohort. ConclusionsPatient engagement and management with a RPM platform are feasible and associated with significantly shorter postoperative length of stay, fewer 30-day readmissions, and an increased rate of discharge to home.

Characterization of peripheral artery disease and associations with traditional risk factors, mobility, and biomarkers in the project baseline health study

There is a dearth of research on immunophenotyping in peripheral artery disease (PAD). This study aimed to describe the baseline characteristics, immunophenotypic profile, and quality of life (QoL) of participants with PAD in the Project Baseline Health Study (PBHS). The PBHS study is a prospective, multicenter, longitudinal cohort study that collected clinical, molecular, and biometric data from participants recruited between 2017 and 2018. In this analysis, baseline demographic, clinical, mobility, QoL, and flow cytometry data were stratified by the presence of PAD (ankle brachial index [ABI] ≤0.90). Of 2,209 participants, 58 (2.6%) had lower-extremity PAD, and only 2 (3.4%) had pre-existing PAD diagnosed prior to enrollment. Comorbid smoking (29.3% vs 14%, P < .001), hypertension (54% vs 30%, P < .001), diabetes (25% vs 14%, P = .031), and at least moderate coronary calcifications (Agatston score >100: 32% vs 17%, P = .01) were significantly higher in participants with PAD than in those with normal ABIs, as were high-sensitivity C-reactive protein levels (5.86 vs 2.83, P < .001). After adjusting for demographic and risk factors, participants with PAD had significantly fewer circulating CD56-high natural killer cells, IgM+ memory B cells, and CD10/CD27 double-positive B cells (P < .05 for all). This study reinforces existing evidence that a large proportion of PAD without claudication may be underdiagnosed, particularly in female and Black or African American participants. We describe a novel immunophenotypic profile of participants with PAD that could represent a potential future screening or diagnostic tool to facilitate earlier diagnosis of PAD. NCT03154346, https://clinicaltrials.gov/ct2/show/NCT03154346.

Biometric description of 34 589 eyes undergoing cataract surgery: sex differences.

To describe gender differences in the biometric parameters of a large sample of patients with cataract. Cataract surgery has evolved from a vision restoration to a refractive procedure, and population-based studies are vital to optimize normative databases and postsurgical outcomes. Miguel Servet University Hospital, Zaragoza, Spain. Retrospective single-center observational study. The study included 34 589 eyes (20 004 patients with cataract). Biometric data were obtained from IOL Master 700 and Pentacam HR. Linear mixed models were used to account for intereye correlation. HofferQST formula was used to calculate the hypothetical distribution of intraocular lens (IOL) power (arbitrary lens; A = 119.2). Most biometric variables showed significant differences between sexes ( P < .0001), such as 0.53 mm shorter eyes found in females, of which 0.16 mm are explained by shorter aqueous depth. Steeper anterior keratometries (∼0.75 diopter [D]) were found in women, to end up in no difference on anterior astigmatism magnitude, but different orientation ( P < .0001). The distribution of IOL power differed between sexes ( P < .001), with the interquartile range shifting 1 D toward more powerful lenses in women and odds ratio (power >26 D) = 2.26, P < .0001 (Fisher). Large sample size studies provide smaller margin of error, higher power, and controlled risk of reporting false (negative or positive) findings. Highly significant differences between sexes in ocular biometry were found; this supports the idea that including sex as a parameter in IOL calculation should be explored and may improve results. In addition, the distribution of IOL powers was provided, which may be useful for manufacturers and hospital stock planning.

Results of the Sports Talent Identification test for Management Information Systems: Borneo Sports Talent ID Software Implementation

This research resulted in a data processing system application program for sports talent identification test results aged 11-15 years, which can accelerate the provision of information about the classification of potentially gifted children and recommendations for sports branches based on anthropometric data parameters and biometric data. The sample in the study was 50 students in Samarinda Kota and Samarinda Ulu sub-districts using the cluster area sampling technique. The data collection technique uses a talent identification test battery instrument. In completing this research, the author uses a database-based programming language in the Borneo Sports Talent ID software. The results showed: (1) Borneo Sports Talent ID can be used as a system for processing data on talent identification results effectively and efficiently, (2) Talent classifications were found, namely: 10% of children have potential, 22% of children have sufficient potential, 32% of children have less potential, and 36% of children have no potential, (3) Recommendations for potential sports: volleyball, rowing, soccer, fencing, swimming, gymnastics, and rock climbing, and athletics in running, jumping, and throwing events.

Clinicopathological study of thyroid gland cancer among selected sample Basrah province between 2019-2022

Background. Thyroid cancer (TC) is the most prevalent cancer within the endocrine system and the head and neck region. Its incidence has been on the rise in Iraq over the past few decades, primarily owing to papillary thyroid carcinoma. This study’s objective is to explore the pathological variants, frequency, and demographic characteristics of TC in a sample of Basrah’s population and subsequently compare these elements with data from other regions in Iraq and globally. Methods. This study is a single-institutional retrospective review conducted at the Department of Pathology within Ports Teaching Hospital in Basrah, Iraq. We analyzed slides of thyroid tumors stained by Hematoxylin and Eosin (H&amp;E) and biometric data gathered from histopathological reports. The cases for this study were identified between 2019 and 2022. Results. In this study, 51 cases were examined, comprised of six males and 45 females. The average age and histopathology patterns were analyzed, finding that the most common type of TC was the classic form of papillary thyroid carcinoma, as seen in 16 cases. The study noted a predominant female presence, accounting for 88.2% of the cases, while males comprised 11.8%. The median age among all subjects was 38 years. Conclusions. A review of all slides featuring thyroid tumors stained with H&amp;E, combined with biometric data from histopathological reports, shows an increasing incidence of TC in Basrah City, Iraq. Predominantly, cases of papillary thyroid carcinoma outnumber those of follicular carcinoma.

Potential for Game-based Assessment of Creativity using Biometric and Real-time Data

Potential for Game-based Assessment of Creativity using Biometric and Real-time Data

Employee Satisfaction on Compensation System: Basis for an Improved Payroll Management System with Face Recognition and Attendance Monitoring Technology

Employee satisfaction with the compensation system is a critical factor influencing organizational productivity, employee retention, and overall workplace morale. Traditional payroll management systems, often plagued by inaccuracies and inefficiencies, can negatively impact employee satisfaction and trust. This study explores the integration of advanced technologies, specifically face recognition and attendance monitoring, into payroll management systems to address these challenges and enhance employee satisfaction.The research investigates the current state of employee satisfaction with existing compensation systems through a comprehensive survey conducted among employees from diverse industries. It identifies key factors contributing to dissatisfaction, such as payroll errors, delays, and perceived lack of transparency. The study then proposes an innovative payroll management system incorporating face recognition technology for accurate attendance tracking and automated payroll processing. Findings suggest that the integration of face recognition and attendance monitoring technology in payroll management systems can significantly enhance employee satisfaction by addressing common pain points and fostering a sense of fairness and transparency. By leveraging biometric data, the proposed system aims to ensure precise and real-time attendance records, thereby reducing payroll errors and administrative burdens. The face recognition technology offers a secure, contactless, and efficient method for attendance monitoring, aligning with modern workplace safety standards and employee preferences. The study concludes with recommendations for organizations considering the adoption of such technologies, emphasizing the importance of user training, data privacy, and continuous system evaluation to ensure long-term success.

육계농장유래 ICT-데이터의 분석을 통해 미생물의 정족수감지 (Quorum Sensing) 현상의 발견을 위한 연구

The current study obtained biometric data yielded by information and communication technology (ICT) equipment installed in the mobile phones of farmers from broiler farms and evaluated quorum sensing (QS) phenomenon (QSP), which can be considered as an emergency rescue request signal for chickens and the starting point of a disease. The results indicated that QSP was identified the most rapidly and sensitively during the analysis of digital data yielded by the ICT-equipped automatic feed feeder, followed by the water consumption data. In particular, in the data on daily feed consumption, QSP was identified to be the point where the consumption rapidly decreased by more than 100% compared with that on the previous day. It is believed that if farm owners responded to the QSP or chicken rescue signals and rapidly implemented disease prevention measures, then it would be the wisest field use of ICT data to minimize loss and damage to farms. In addition, the results of this study revealed the priority order of the introduction of ICT equipment. and the most urgent and important for farm management is daily feed consumption data by applying the scientifically based QS approach, for the first time, to our knowledge, in Korea. The results of this study uncovered the importance of installing ICT equipments.