Biometric Data Research Articles

Human-machine interaction with intelligent machine learning algorithms for smart classrooms

ABSTRACT The modern technology of learning and teaching in education has moved on to smart classes. The introduction of artificial intelligence (AI) in the education sector has essentially taken over traditional classrooms and transformed how education leads to admiration. Various data types, such as videos, images, sound, text, and biometric data like eye-tracking potentially captured through smart classrooms, pose new issues and challenges for artificial intelligence algorithms. Hence, this paper suggests the Machine Learning Assisted Intelligent Human-Machine Interaction (MLIHMI) model for the smart classroom monitoring system. The real-time interactive dimension signifies the capability to support the Smart classroom’s teaching and human-computer interactions, including smooth interaction, convenient operation, and interactive monitoring. This paper used ML and HMI models to introduce a new effective database for online learning and smart classroom environments utilising the learners’ body postures, facial expressions, and hand gestures. The numerical findings show that the suggested system achieves less error rate of 10.1% and enhances the accuracy rate of 96.5%, with the highest classification ratio of 95.6%, prediction rate of 93.6%, the detection rate of 97.3%, and engagement rate of 81.3% in student gesture and postures recognition and the identification of facial expressions with the high performance of 93.3% when compared to other existing models.

Universal Fingerprint Generation: Controllable Diffusion Model with Multimodal Conditions.

The utilization of synthetic data for fingerprint recognition has garnered increased attention due to its potential to alleviate privacy concerns surrounding sensitive biometric data. However, current methods for generating fingerprints have limitations in creating impressions of the same finger with useful intra-class variations. To tackle this challenge, we present GenPrint, a framework to produce fingerprint images of various types while maintaining identity and offering humanly understandable control over different appearance factors such as fingerprint class, acquisition type, sensor device, and quality level. Unlike previous fingerprint generation approaches, GenPrint is not confined to replicating style characteristics from the training dataset alone: it enables the generation of novel styles from unseen devices without requiring additional fine-tuning. To accomplish these objectives, we developed GenPrint using latent diffusion models with multimodal conditions (text and image) for consistent generation of style and identity. Our experiments leverage a variety of publicly available datasets for training and evaluation. Results demonstrate the benefits of GenPrint in terms of identity preservation, explainable control, and universality of generated images. Importantly, the GenPrint-generated images yield comparable or even superior accuracy to models trained solely on real data and further enhances performance when augmenting the diversity of existing real fingerprint datasets.

Insights into the Sex-Related Effects of Dietary Polyphenols and Metabolic Disruptors on Inflammatory and (Neuro) Endocrine Pathways in Obesity: The HEAL Project

Background: this study was performed under the umbrella of the Health Extended Alliance for Innovative Therapies, Advanced Lab Research, and Integrated Approaches of Precision Medicine (HEAL ITALIA) partnership and funded under the National Recovery and Resilience Plan, Mission 4 Component 2 Investment 1.3, and by the European Union. Objectives: the overall objective of the HEAL project is to identify innovative and effective therapeutic approaches to reduce disease burden. The present research falls within Spoke 7: Prevention Strategies: Integrated and gender medicine approaches for prevention strategies based on environmental, lifestyle, and clinical biometric data. Obesity represents a primary risk factor worldwide for the onset of numerous life-threatening diseases, including metabolic, cardiovascular, and neurological diseases. Environmental and gender-related factors influence obesity development. However, the molecular mechanisms underlying the effects of those agents on different organs of the human body are not fully understood yet. Methods: here, we present a study protocol aimed at shedding light on (i) the complex interplays among adipose tissue, brain and gut in obesity, and (ii) the effects of specific dietary components and environmental metabolism-disrupting compounds on those interactions. To this purpose, we combined ex vivo, in vitro, and in vivo approaches to gain additional knowledge about the molecular mechanisms underlying connections between organs. Conclusions: the data provided by this study will contribute to defining new targets for therapeutic and/or preventive interventions, thereby allowing more personalized approaches to nutrition.

Biometric and Geographic Information-based Identity Authentication Method

With the ongoing advancements in computer technology and mobile internet, mobile payment has become an integral component of daily life. Identity authentication technology serves as a critical measure to ensure the security of mobile payments. This paper addresses the issue of low security associated with single-factor identity authentication while highlighting the substantial overhead involved in multi-factor authentication methods. We propose a novel identity authentication method that integrates biometric data and geographic information. In this framework, the identity authentication server first performs system initialization, configures system parameters, and stores them securely. Subsequently, the identity authentication client submits an authentication request to the server and completes its registration process. Finally, the server processes this request from the client, conducts user identity verification, and returns an authentication result. This approach effectively mitigates risks posed by attackers attempting to falsify biometric data or geographic information during the identity verification process and offers robust services for biometric- and geography-based identity authentication.

Deep Dive Into Noninvasive Biometrics: A Pilot Journey Using Stereo-Video in a Public Aquarium.

Accurate collection of biometric data is important for understanding the biology and conservation of marine organisms, including elasmobranch and teleost fish, both in nature and controlled environments where monitoring marine specimens' health is mandatory. Traditional methods involving specimen capture and handling are invasive, stressful, and disruptive. Some techniques like underwater visual census or laser photogrammetry have been used for noninvasive data collection, but they have limitations and biases. The application of stereo-video photogrammetry through the use of diver-operated stereo-video systems (stereo-DOV) is a noninvasive method that overcomes these challenges, providing highly accurate measurements. It has become popular for species monitoring, studying anthropogenic impacts, and assessing length distributions. However, this technique is still uncommon and barely reported in aquarium settings. This study describes an innovative pilot study targeting multiple species carried out in a Public Aquarium, using a low-cost house-made device. The results revealed that measuring more than 100 individuals in approximately 1 day's work is possible. Total and fork lengths were estimated using specific software for 31 teleost and 16 elasmobranch species and compared with real measurements for the available species. Despite technical limitations that must be reviewed for application in future studies that resulted in high root mean square (RMS) values (> 20 mm), differences between methodological approaches revealed a minimal discrepancy (1.37%-5% in large sharks and rays and 1.8%-5.5% in teleost fish). This technique has time and cost requirements, but might represent a major advance in husbandry and in the contribution to conservation that ex situ studies can provide.

Preoperative nTMS analysis: a sensitive tool to detect imminent motor deficits in brain tumor patients.

One of the challenges in surgery of tumors in motor eloquent areas is the individual risk assessment for postoperative motor disorder. Previously a regression model was developed that permits estimation of the risk prior to surgery based on topographical and neurophysiological data derived from investigation with nTMS (navigated Transcranial Magnetic Stimulation). This study aims to analyze the impact of including additional neurophysiological TMS parameters into the established risk stratification model for motor outcome after brain tumor surgery. Biometric and clinical data of 170 patients with glioma in motor eloquent areas were collected prospectively. In addition, the following nTMS parameters were collected bihemispherically prior to surgery: resting motor threshold (RMT), recruitment curve (RC), cortical silent period (CSP) and a nTMS based fibertracking to measure the tumor tract distance (TTD). Motor function was quantified by Medical Research Council Scale (MRCS) preoperatively, seven days and three months postoperatively. Association between nTMS parameters and postoperative motor outcome was investigated in bivariate and multivariable analyses. The bivariate analysis confirmed the association of RMT ratio with the postoperative motor outcome after seven days with higher rates of worsening in patients with RMT ratio > 1.1 compared to patients with RMT ratio ≤ 1.1 (31.6% vs. 15.1%, p = 0.009). Similarly, an association between a pathological CSP ratio and a higher risk of new postoperative motor deficits after seven days was observed (35.3% vs. 16.7% worsening, p = 0.025). A pathological RC Ratio was associated postoperative deterioration of motor function after three months (42.9% vs. 16.2% worsening, p = 0.004). In multiple regression analysis, none of these associations were statistically robust. The current results suggest that the RC ratio, CSP ratio and RMT ratio individually are sensitive markers associated with the motor outcome 7days and 3months after tumor resection in a presumed motor eloquent location. They can therefore supply valuable information during preoperative risk-benefit-balancing. However, underlying neurophysiological mechanisms might be too similar to make the parameters meaningful in a combined model.

A Performance Benchmark for the PostgreSQL and MySQL Databases

This study highlights the necessity for efficient database management in continuous authentication systems, which rely on large-scale behavioral biometric data such as keystroke patterns. A benchmarking framework was developed to evaluate the PostgreSQL and MySQL databases, minimizing repetitive coding through configurable functions and variables. The methodology involved experiments assessing select and insert queries under primary and complex conditions, simulating real-world scenarios. Our quantified results show PostgreSQL’s superior performance in select operations. In primary tests, PostgreSQL’s execution time for 1 million records ranged from 0.6 ms to 0.8 ms, while MySQL’s ranged from 9 ms to 12 ms, indicating that PostgreSQL is about 13 times faster. For select queries with a where clause, PostgreSQL required 0.09 ms to 0.13 ms compared to MySQL’s 0.9 ms to 1 ms, making it roughly 9 times more efficient. Insert operations were similar, with PostgreSQL at 0.0007 ms to 0.0014 ms and MySQL at 0.0010 ms to 0.0030 ms. In complex experiments with simultaneous operations, PostgreSQL maintained stable performance (0.7 ms to 0.9 ms for select queries during inserts), while MySQL’s performance degraded significantly (7 ms to 13 ms). These findings underscore PostgreSQL’s suitability for environments requiring low data latency and robust concurrent processing capabilities, making it ideal for continuous authentication systems.

Cryodehydration applied to bovine fetal hearts: A model for studying cardiac organogenesis.

The study aimed to standardize the cryodehydration technique for bovine fetal hearts, focusing on optimizing protocols for each developmental stage to preserve morphological characteristics. We analyzed 29 bovine fetal hearts categorized into early, middle, and late developmental stages. These hearts underwent cryodehydration until a 60%-70% reduction in original fluid volume was achieved. Biometric data were recorded and statistically analyzed using Pearson correlation tests for age versus weight and age versus number of cryodehydration sessions. Morphometric comparisons before and after cryodehydration were performed using paired t-tests. In Group I, hearts exhibited well-defined structures, including the atrium cordis, ventriculus cordis, auricula atrii, aorta, truncus pulmonalis, and ramus coronaries arteria, which were preserved in Groups II and III. Additionally, in Group I the heart had a conical or flat apex cordis, whereas those in Groups II and III had a more pronounced apex. The average number of cryodehydration sessions required was 9.38 (±1.2) days for Group I, 12.37 (±1.4) days for Group II, and 15 days for Group III. A positive correlation was found between age and sample weight, indicating that more developed hearts were heavier. Similarly, there was a positive correlation between gestational age and the number of cryodehydration sessions, suggesting that more advanced stages required more cryodehydration sessions. Paired t-tests demonstrated high statistical significance in the morphometric parameters before and after cryodehydration, indicating a loss of mass during dehydration. However, there was no alteration in the macroscopic structure of the hearts, which remained morphologically preserved. In conclusion, cryodehydration shows promise for preserving and analyzing the external morphological characteristics of bovine fetal cardiac development. It also provides lightweight, odorless, and easy-to-handle specimens ideal for detailed morphological studies and offers a unique perspective for investigating cardiac morphology in biological research contexts.

Genetic Analysis of Combining Ability and Gene Action in Sunflower (Helianthus annuus L.) Hybrids through Line × Tester Design

This study was conducted to evaluate the combining ability of six cytoplasmic male sterile (CMS) lines and nine testers using the line × tester method. Biometrical data were recorded for ten agronomic traits: days to 50% flowering, days to maturity, plant height, head diameter, seed filling percentage, 100-seed weight, volume weight, hull content, oil content, and seed yield per plant. The results indicated that specific combining ability (SCA) variance was higher than general combining ability (GCA) variance for most traits, except for days to 50 percent flowering and seed filling percentage, suggesting that non-additive gene action predominantly governed the inheritance of these traits. The GCA of parents did not always align with their per se performance, indicating that GCA should be the primary criterion for selecting parents in breeding programs. Three CMS lines, CMS-103A, CMS-10A and CMS-112A, along with testers EC-75717, EC-502036, EC-279309-1 and R-271-1, were identified as good general combiners for seed yield and yield-contributing traits. Among the hybrids, CMS-243A × EC-279309-1, CMS-243A × EC-75717 and CMS-103A × EC-75717 exhibited the highest SCA effects for seed yield per plant. These findings underscore the importance of selecting superior general combiners for traits like oil content and seed yield to develop high-yielding sunflower hybrids.

Development of professional skills in kickboxing athletes using virtual reality technologies

Ukrainian sports are currently facing numerous problems due to the hostile military operations by the Russian Federation, leading to the emigration of gifted athletes from the nation. Considering these challenges, the search for novel ways to support and enhance athletes throughout their careers has become critically important. Cutting-edge technologies, especially virtual reality (VR), are being proposed as a potent means to enhance professional training. They facilitate the fusion of traditional and innovative methods, ensuring that athletic training evolves to meet contemporary demands. This strategy adjusts the requirements of athletes across the spectrum, from beginners to professionals, as well as mentors and sports entities eager to incorporate unconventional technologies into their training regimen. Employing immersive VR settings enables the replication of authentic competitive scenarios and training intensities. It offers the capability to monitor biometric data, evaluating the athletes' resilience to stress, their focus, and their strategic expertise. Anticipated outcomes of this holistic approach include enhanced training session productivity, reinforced mental strength in competitors during events, and expedited recuperation from injuries. The introduction and application of VR technology in sports training signifies a leap towards revitalizing traditional training techniques, ensuring they are modular and adaptable. This facilitates seamless integration into a range of sports disciplines and alignment with diverse training schemes, enhancing Ukraine's global sports standing and positioning the nation as a pioneer in athlete training innovation. In this study, we explored the incorporation of VR technology in kickboxing, specifically targeting the 10–12-year age bracket. At this developmental stage, children are particularly receptive to new experiences and stimuli, making such a technologically enriched environment a powerful tool to boost training enthusiasm. Virtual reality holds the promise of breaking the routine of standard training practices, rendering them more captivating and interactive, which is crucial for attracting young participants.

Intensive, Real-Time Data Collection of Psychological and Physiological Stress During a 96-Hour Field Training Exercise at a Senior Military College: Feasibility and Acceptability Cohort Study.

Poor physical fitness, stress, and fatigue are factors impacting military readiness, national security, and economic burden for the United States Department of Defense. Improved accuracy of wearable biosensors and remote field biologic sample collection strategies could make critical contributions to understanding how physical readiness and occupational stressors result in on-the-job and environment-related injury, sleep impairments, diagnosis of mental health disorders, and reductions in performance in war-fighters. This study aimed to evaluate the feasibility and acceptability of intensive biomarker and biometric data collection to understand physiological and psychological stress in Army Reserved Officer Training Corps cadets before, during, and after a 96-hour field training exercise (FTX). A prospective pilot study evaluated the feasibility and acceptability of multimodal field data collection using passive drool saliva sampling, sweat sensors, accelerometry, actigraphy, and photoplethysmography. In addition, physical fitness (Army Combat Fitness Test), self-reported injury, and psychological resilience (Brief Resilience Scale) were measured. A total of 22 cadets were included. Two were lost to follow-up due to injury during FTX, for a retention rate of 91%. Assessments of performance and psychological resilience were completed for all remaining participants, resulting in 100% testing adherence. All participants provided saliva samples before the FTX, with 98% adherence at the second time point and 91% at the third. For sweat, data collection was not possible. Average daily wear time for photoplethysmography devices was good to excellent, meeting a 70% threshold with data collected for ≥80% of person-days at all time points. Of the participants who completed the FTX and 12 completed a post-FTX acceptability survey for a response rate of 60%. Overall, participant acceptance was high (≥80%) for all metrics and devices. This study demonstrates that wearable biosensors and remote field biologic sample collection strategies during a military FTX have the potential to be used in higher stakes tactical environments in the future for some, but not all, of the strategies. Overall, real-time biometric and biomarker sampling is feasible and acceptable during field-based training and provides insights and strategies for future interventions on military cadet and active-duty readiness, environmental stress, and recovery.

Morphometric Tomographic Analysis of the Head of the Tridactyl Humanoid Specimen from Nasca-Peru

Objective: To report the tomographic and morphometric analysis of the head of the tridactyl humanoid specimen from Nazca, Peru. Method: A qualitative case study approach. RadiAnt DICOM Viewer software version 4.2.1 was used to analyze lengths, diameters, and volume in tomographic images of the head. The morphological biometric analysis applied anthropometric methods using some craniometric points and cephalometric angles. Results and Discussion: As a result, it is reported that there are atypical, strange, and exceptional morphometric imaging features in the cephalic segment of specimen M01. The cephalometric analysis based on the ANB angle revealed a class II skeletal pattern, and according to the Bjorl-Jarabak analysis, it has a convex facial profile with hypodivergent growth (brachyfacial). The patient also has maxillary biprotrusion, severe protrusion of upper and lower incisors, loss of teeth, presence of root remnants, severe dental abrasion, non-assessable canine class, absence of three third molars and ocular protrusion. Based on craniometric points, the SNA angle (Sella, Nasion and Point A) and the SNB angle (Sella, Nasion and Point B) maxillary protrusion and mandibular protrusion were diagnosed. Implications of the research: It is concluded that the combination of uncommon morpho-anatomical features in the maxillofacial and cranial massif do not correspond to a human craniofacial biotype, but could be considered as suggestive findings of morpho-anatomical features typical of a hominid species similar to humans. Originality/Value: The expansion of biometric and morphoanatomical data of the head reinforces the hypothesis that morphologically it would be a non-human hybrid species.

Conditional Deployable Biometrics: Matching Periocular and Face in Various Settings

In this paper, we introduce the concept of Conditional Deployable Biometrics (CDB), designed to deliver consistent performance across various biometric matching scenarios, including intra-modal, multimodal, and cross-modal applications. The CDB framework provides a versatile and deployable biometric authentication system that ensures reliable matching regardless of the biometric modality being used. To realize this framework, we have developed CDB-Net, a specialized deep neural network tailored for handling both periocular and face biometric modalities. CDB-Net is engineered to handle the unique challenges associated with these different modalities while maintaining high accuracy and robustness. Our extensive experimentation with CDB-Net across five diverse and challenging in-the-wild datasets illustrates its effectiveness in adhering to the CDB paradigm. These datasets encompass a wide range of real-world conditions, further validating the model’s capability to manage variations and complexities inherent in biometric data. The results confirm that CDB-Net not only meets but exceeds expectations in terms of performance, demonstrating its potential for practical deployment in various biometric authentication scenarios.

Cyber-Securing Medical Devices Using Machine Learning: A Case Study of Pacemaker

This study aims to enhance the cybersecurity framework of pacemaker devices by identifying vulnerabilities and recommending effective strategies. The objectives are to pinpoint cybersecurity weaknesses, utilize machine learning to predict security breaches, and propose countermeasures based on analytical trends. The literature review highlights the transformation of pacemaker technology from basic, fixed-rate devices to sophisticated systems with wireless capabilities, which, while improving patient care, also introduce significant cybersecurity risks. These risks include unauthorized entry, data breaches, and life-threatening device malfunctions. The methodology in this study utilizes a quantitative research approach using the WUSTL-EHMS-2020 dataset, which includes network traffic features, patients' biometric features, and attack label. The step-by-step method of machine learning prediction includes data collection, data preprocessing, feature engineering, and models’ training using Support Vector Machines (SVM) and Gradient Boosting Machines (GBM). The implementation results used evaluation metrics like accuracy, precision, recall, and F1 score to show that GBM model outperformed the SVM model. The GBM model achieved higher accuracy of 95.1% compared to 92.5% for SVM, greater precision of 99.6% compared to 96.7% for SVM, better recall of 94.9% compared to 42.7% for SVM, and a higher F1 score of 76.3% compared to 59.0% for SVM, making GBM model more effective in predicting cybersecurity threats. This study concludes that GBM is an effective machine learning model for enhancing pacemaker cybersecurity by analyzing network traffic and biometric data patterns. Future recommendations for improving the pacemaker cybersecurity include implementing GBM model for threat predictions, integration with existing security measures, and regular model updates and retraining.

Case C-80/23: A Quest for Clarity. The Law Enforcement Directive 2016/680, a Bulgarian Court and Case C-205/21

Abstract This contribution examines the legal intricacies surrounding law enforcement data collection practices in Bulgaria within the framework of the Law Enforcement Directive 2016/680 (LED) and relevant Court of Justice of the European Union (CJEU) rulings, particularly cases C-205/21 and C-80/23. The analysis underscores challenges in interpreting the concept of “strict necessity” and ensuring compliance with its provisions, in particular, Art. 10. Key findings reveal ambiguities in the LED’s application, particularly concerning judicial review and the scope of data collection. The subsequent Case C-80/23 further seeks clarification on the strict necessity standard and the scope of judicial review in the collection of biometric and genetic data. The outcome of both cases has broader implications for EU member states, highlighting the need for legislative alignment and underscoring the complexities and challenges in balancing effective law enforcement with the protection of fundamental rights.

Nitrogen metabolism in blackmoiled cattle of Kuzbass

A research analysis of nitrogen metabolism in black-and-white cattle was carried out in the conditions of the Kuzbass region. Healthy animals at the age of 2nd lactation with a productivity level from 7300 to 10500 kg were selected for the experimental group. Laboratory studies were carried out at the Department of Veterinary Genetics and Biotechnology of the Novosibirsk State Agrarian University. Indicators of nitrogen metabolism in blood serum were assessed photometrically on a biochemical semi-automatic analyzer Photometer 5010 V5+. Reagent kits from Vector-Best and Olvex-Diagnosticum were used. When biometric data processing, standard descriptive statistics techniques and robust indicators were used. The Anderson-Darling test was used to assess the normality of data distribution. .When calculating correlation relationships, the Pearson coefficient was used. Calculations were performed in the “RStudio” programming environment. The obtained average values of nitrogen metabolism indicators were within the physiological norm. The strongest positive associations were observed between urea levels and uric acid, and urea and creatinine. No connection was found between other indicators of nitrogen metabolism. There were no differences in milk yield between cows with high and low levels of nitrogen metabolism. Graphs of the range of nitrogen content in black-and-white cows during lactation are presented. Calculations of reference indicators based on data from Kuzbass dairy cattle make it possible to more accurately assess the clinical condition of animals and timely detect deviations from established standard values. It is also necessary to take into account reference limits when monitoring the quality and completeness of cattle.

Automated Detection of Neurodevelopmental Disorders Using Face-to-Face Mobile Technology Among Typically Developing Greek Children: Randomized Controlled Trial.

Neurodevelopmental disorders (NDs) are characterized by heterogeneity, complexity, and interactions among multiple domains with long-lasting effects in adulthood. Early and accurate identification of children at risk for NDs is crucial for timely intervention, yet many cases remain undiagnosed, leading to missed opportunities for effective interventions. Digital tools can help clinicians assist and identify NDs. The concept of using serious games to enhance health care has gained attention among a growing group of scientists, entrepreneurs, and clinicians. This study aims to explore the core principles of automated mobile detection of NDs in typically developing Greek children, using a serious game developed within the SmartSpeech project, designed to evaluate multiple developmental domains through principal component analysis (PCA). A total of 229 typically developing children aged 4 to 12 years participated in the study. The recruitment process involved open calls through public and private health and educational institutions across Greece. Parents were thoroughly informed about the study's objectives and procedures, and written consent was obtained. Children engaged under the clinician's face-to-face supervision with the serious game "Apsou," which assesses 18 developmental domains, including speech, language, psychomotor, cognitive, psychoemotional, and hearing abilities. Data from the children's interactions were analyzed using PCA to identify key components and underlying principles of ND detection. A sample of 229 typically developing preschoolers and early school-aged children played the Apsou mobile serious game for automated detection of NDs. Performing a PCA, the findings identified 5 main components accounting for about 80% of the data variability that potentially have significant prognostic implications for a safe diagnosis of NDs. Varimax rotation explained 61.44% of the total variance. The results underscore key theoretical principles crucial for the automated detection of NDs. These principles encompass communication skills, speech and language development, vocal processing, cognitive skills and sensory functions, and visual-spatial skills. These components align with the theoretical principles of child development and provide a robust framework for automated ND detection. The study highlights the feasibility and effectiveness of using serious games for early ND detection in children. The identified principal components offer valuable insights into critical developmental domains, paving the way for the development of advanced machine learning applications to support highly accurate predictions and classifications for automated screening, diagnosis, prognosis, or intervention planning in ND clinical decision-making. Future research should focus on validating these findings across diverse populations integrating additional features such as biometric data and longitudinal tracking to enhance the accuracy and reliability of automated detection systems. ClinicalTrials.gov NCT06633874; https://clinicaltrials.gov/study/NCT06633874. RR2-https://doi.org/10.3390/signals4020021.

Secured Fog-Body-Torrent : A Hybrid Symmetric Cryptography with Multi-layer Feed Forward Networks Tuned Chaotic Maps for Physiological Data Transmission in Fog-BAN Environment

Recently, the Wireless Body Area Networks (WBAN) have become a promising and practical option in the tele-care medicine information system that aids for the better clinical monitoring and diagnosis. The trend of using Internet of Things (IoT) has propelled the WBAN technology to new dimension in terms of its network characteristics and efficient data transmission. However, these networks demand the strong authentication protocol to enhance the confidentiality, integrity, recoverability and dependability against the emerging cyber-physical attacks owing to the exposure of the IoT ecosystem and the confidentiality of biometric data. Hence this study proposes the Fog based WBAN infrastructure which incorporates the hybrid symmetric cryptography schemes with the chaotic maps and feed forward networks to achieve the physiological data info security without consuming the characteristics of power hungry WBAN devices. In the proposed model, scroll chaotic maps are iterated to produce the high dynamic keys streams for the real time applications and feed-forward layers are leveraged to align the complex input-output associations of cipher data for subsequent mathematical tasks. The feed forward layers are constructed which relies on the principle of Adaptive Extreme Learning Machines (AELM) thereby increasing randomness in the cipher keys thereby increasing its defensive nature against the different cyber-physical attacks and ensuring the high secured encrypted-decrypted data communication between the users and fog nodes. The real time analysis is conducted during live scenarios. BAN-IoT test beds interfaced with the heterogeneous healthcare sensors and various security metrics are analysed and compared with the various residing cryptographic algorithms. Results demonstrates that the recommended methodology has exhibited the high randomness characteristics and low computational overhead compared with the other traditional BAN oriented cryptography protocol schemes

Biometric data: Specific Features of Legislative Regulation in Russia

Biometric data: Specific Features of Legislative Regulation in Russia

Biometrically measured sleep in medical students as a predictor of psychological health and academic experiences in the preclinical years

ABSTRACT Background Student wellness is of increasing concern in medical education. Increased rates of burnout, sleep disturbances, and psychological concerns in medical students are well documented. These concerns lead to impacts on current educational goals and may set students on a path for long-term health consequences. Methods Undergraduate medical students were recruited to participate in a novel longitudinal wellness tracking project. This project utilized validated wellness surveys to assess emotional health, sleep health, and burnout at multiple timepoints. Biometric information was collected from participant Fitbit devices that tracked longitudinal sleep patterns. Results Eighty-one students from three cohorts were assessed during the first semester of their M1 preclinical curriculum. Biometric data showed that nearly 30% of the students had frequent short sleep episodes (<6 hours of sleep for at least 30% of recorded days), and nearly 68% of students had at least one episode of three or more consecutive days of short sleep. Students that had consecutive short sleep episodes had higher rates of stress (8.3%) and depression (5.4%) symptoms and decreased academic efficiency (1.72%). Conclusions Biometric data were shown to significantly predict psychological health and academic experiences in medical students. Biometrically assessed sleep is poor in medical students, and consecutive days of short sleep duration are particularly impactful as it relates to other measures of wellness. Longitudinal, biometric data tracking is feasible and can provide students the ability to self-monitor health behaviors and allow for low-intensity health interventions.