Biometric Sensors Research Articles

Evolving dementia care: An explorative study on the lived experience of older adults living with dementia in nursing homes using observational and biometric sensor data.

This study explores the lived experiences of older adults with dementia in Dutch nursing homes, focusing on daily activities and emotional responses. With a growing number of older adults with dementia, gaining a deeper understanding of their lived experience is imperative. Using a mixed-method narrative approach involving observations, informal interviews, and physiological monitoring through wearable sensors, the study engaged eight participants in psychogeriatric wards across two Dutch nursing homes. Observations and interviews aimed to provide context to daily activities, while wearable sensors tracked emotional responses through heart rate (HR) and heart rate variability (HRV). Key activities included eating, drinking, communication, mobility, and inactivity. Positive experiences were consistently observed during eating and drinking and communication, respectively influenced by the nursing home's social and organizational structures and social and personal contexts. In contrast, mobility and inactivity exhibited diverse physiological responses, reflecting a range of stress, concentration, or relaxation. This study offers valuable insights into the lived experiences of older adults with dementia in nursing homes. It highlights the generally positive nature of eating and drinking, shaped by social and organizational factors. Communication's impact varies with individual context. The study also reveals a complex interplay of emotions during activities related to mobility and inactivity, as evidenced by diverse physiological responses. Regarding implications for dementia care, the study emphasizes the need to redefine 'inactivity' as 'under-stimulation,' assess appropriate (in)activity levels, and acknowledge the significance of the nursing home's physical and organizational context. This redefinition should distinguish between 'physical' and 'mental' inactivity, address concerns related to under-stimulation, and cater to individual preferences. Recognizing the residents' restricted environment and reliance on care professionals and volunteers underscores the urgency of tailored approaches. Addressing these implications can provide fresh perspectives for evolving dementia care, creating a more supportive environment that promotes well-being.

Human motion and musical expression: Exploring the link between physical movement and emotional resonance in performance

Musical performance has traditionally been understood as an auditory experience, with emotions conveyed primarily through sound. However, emerging research highlights the role of physical movement—gestures, posture, and body language—in enhancing emotional expression and creating a deeper connection between performers and their audiences. While existing studies have explored some aspects of this relationship, there is limited understanding of how specific movements modulate emotional responses across different musical genres. This study addresses these gaps by investigating the interaction between human motion and musical expression, focusing on the emotional resonance experienced by performers and audience members. The need for this study arises from the lack of comprehensive data on the correlation between movement and emotional engagement during musical performances, particularly concerning different genres. Using motion capture, biometric sensors, and facial recognition technology, the research analyzed the performances of professional and amateur musicians across classical, jazz, and contemporary music. Audience members’ emotional responses were captured through physiological data and post-performance surveys. One limitation of this study is that it focused solely on live performance environments, leaving digital or virtual performances unexplored. Key findings reveal that expressive movements increased emotional intensity ratings by 1.6 points (p < 0.001), with heart rate rising by 6.9 BPM (p < 0.0012) and electrodermal activity increasing by 1.6 µS (p < 0.0008). Movement synchronization with musical elements, especially during climaxes, strongly correlated with heightened emotional responses (r = 0.85, p < 0.01). Classical performances exhibited the highest synchronization between movement and emotion, with audience emotional intensity peaking at 8.3 on a Likert scale.

Advancing Measurement of Correctional Officer Health and Wellness: A Pilot Study of Wearable Biometric Sensor Devices

Correctional work is associated with high levels of stress and poor sleep quality. Although previous research has primarily relied on correctional officers’ perceptions of these outcomes, biometric indicators can provide more objective and continuous information. Using daily data points collected from smartwatches worn by 15 prison staff over a six-week period with a single-subject ABA research design involving two control phases and a mindfulness activity phase, this pilot study establishes a proof of concept for using wearable biometric sensor devices to study correctional staff health and well-being. By applying longitudinal mixed effects regression models with a GAMLSS framework, this study finds that biometric measures are sensitive to change, such that stress was reduced and sleep quality improved among participants during the mindfulness activity phase. Participating staff complied with and felt positively about the research protocols. Several important lessons for advancing measurement in future correctional health and wellness research are discussed.

Image sensor fusion for multimodal biometric recognition in mobile devices

AimMultimodal biometric recognition authenticates or identifies someone using various biometric features or modalities. Multimodal biometric systems use different biometric qualities to improve the recognition process's accuracy, security, and reliability rather than depending solely on one biometric property, such as fingerprint or facial recognition. Sensor fusion techniques can merge data from several biometric sensors, including fingerprint scanners and facial recognition cameras, to boost the accuracy and dependability of biometric authentication in the context of multimodal biometric recognition in mobile devices. The process of combining data from numerous image sensors to enhance a system's overall performance is known as image sensor fusion. ChallengesChallenges includeintegration difficulties in achieving accurate and secure multimodal biometric recognition on mobile devices. The study goal was Image sensor fusion for multimodal biometric recognition in mobile devices. MethodologyThe study uses a specially made database of 450 face photos roughly 450 fingerprints.Weiner filter (WF) is used for preparing data. Battle Royale optimized with deep convolutional neural network (BRO-DCNN) is proposed to improve the overall performance of fingerprint and facial information. FindingsAccuracy, sensitivity, specificity, and precision are used to measure the effectiveness of the proposed BRO-DCNN system. Study results show that BRO-MLANN solves the many characteristics of smart cities and is an efficient approach compared to other current methods in recognition rate, 98 % accuracy, 99 % precision, 96 % specificity, and 94 % sensitivity.

The Sensors for stress detection: first analysis

Since 2020, due to the pandemic caused by the COVID-19 virus, the world has come to a standstill, causing the isolation of the population. The vulnerability perceived by the population, as well as the lock-down, have wreaked havoc on the physical and emotional health of most people. One of the main conditions has been stress, which can trigger other conditions such as heart issues, anger, depression, among others. Therefore, at the Autonomous University of Hidalgo State, in the Software Engineering degree program and in collaboration with institutes as the Technologic Institute of Zacatepec, we have proposed to implement a development based on Arduino and biometric sensors which allow monitoring bio-signals to determine the user's perceived level of stress, through connection with a mobile application. For this reason, this document is based on researching, comparing, and analyzing the sensors to be applied to the final project.

456 Allocating distribution of pasture utilization across the grazing landscape in grazing steers equipped with virtual fencing collars

Abstract Spatial utilization of pasture landscapes by grazing animals is often heterogeneous and driven by complex environmental and physiological interactions between plants and animals. Synergistic energy pathways can be modeled using mechanistic relationships describing resource distribution, animal behavior, metabolic energy rate, and pasture utilization using biometric sensors. The objective of this study was to demonstrate the ability to measure the energy landscape by mapping fine-scale contributions to animal energy expenditure, nutrient acquisition, growth, and overall economic profitability. We utilized animal trajectories and location fixes recorded by virtual fencing collars (Vence) daily body weight (BW) using precision livestock scales (Smartscale, C-Lock Inc.) and oxygen consumption (GreenFeeds, C-Lock Inc.) on steers (n = 127), managed as part of a broader project to evaluate environmental synergies in extensive grazing systems. Steers were allocated to one of six native grass pastures assigned to either rotational or continuous grazing strategies, and one of three stocking rates (low, moderate, high) in a 2 x 3 factorial arrangement. Space utilization was quantified as the frequency of fixes located within each cell of a 5 x 5-meter Grid and the distance between grid cells and water was calculated using the Pythagorean theorem. Movement behavior was quantified using Program R to calculate step length, the distance between two temporally continuous fix points utilizing the Pythagorean Theorem, and turn angles calculated as the cosine of the pre- and pro-ceeding step lengths where larger values indicated greater deviation from a straight trajectory. A mixed linear regression model was fitted using the lmer function in the lme4 package. Space utilization decreased with increased distance from water (P < 0.05), with rotational grazing strategies increasing concentration around water sources (P < 0.05). This indicates an opportunity to utilize precision livestock technology to facilitate pasture management and increase space utilization in grazing cattle.

Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

Predicting Epileptic Seizures Using Biometric Sensors - A Pilot Study Protocol.

Epilepsy is a prevalent neurological disorder characterized by unpredictable seizures despite some known risk factors. Using an mHealth system and Empatica EmbracePlus, we aim to examine if biometric sensor data can detect and predict epileptic seizures. Five participants with epilepsy will be followed for a minimum of 15 seizures. Expected results and possible implications are presented. This study may provide new insights into biological indicators for seizure forecasting.

EFFICACY OF VIRTUAL REALITY EXPOSURE THERAPY FOR THE TREATMENT OF POST TRAUMATIC STRESS DISORDER IN ADULTS

The emergence of new technologies like enhanced immersion and realism providing realistic stimulations, biometric sensor integration having physiological monitoring like heart rate monitors and conductance sensors which allow the observation and monitoring of patients behaviour and their responses and combining the reality with VR technology have altogether made the Virtual Reality Exposure Therapy more effective for the treatment of phobias and Post Traumatic Stress Disorder [1]. The efficacy of VRET in treatment of PTSDcan be observed through various studies conducted on war veterans, general population and people suffering from stressful traumatic memories of the past.Astudy was conducted to assess the treatment ofPTSD through VRET in the survivors of 9/11 attacks revealed significant reductions in symptoms with large effect size (d=1.50).

Peningkatan Kompatibilitas Sistem Operasi Windows di Berbagai Perangkat

The goal of improving the compatibility of the Windows operating system with various devices is to ensure that Windows runs well on desktops, laptops, tablets, and mobile devices. Technical developments such as system modularization, performance optimization, and better driver integration are part of this effort. To expand the range of devices, Microsoft supports ARM architecture in addition to x86 architecture. Additionally, support for IoT technology, biometric sensors and touch screens has been improved. Maintaining a consistent user experience and expanding the ecosystem of Windows-compatible devices is the goal of this effort.

A Comprehensive Review- Building A Secure Social Media Environment for Kids- Automated Content Filtering with Biometric Feedback

This review paper is all about how important it is to use smart technology to keep kids safe on social media while helping them learn better. By adding things like better controls for parents, filters that stop bad stuff, and tools that check how kids are feeling, we can make sure they don't run into anything harmful online. In today's world where kids spend a lot of time online, it's super important to make sure they're safe. If social media platforms start using cool new tech like biometric sensors and wearable gadgets, they can create safer spaces for kids to have fun and learn. This paper also talks about why we need to do things ahead of time to deal with problems like spending too much time on screens or seeing things that might not be right for us. By giving practical ideas for researchers, people who make rules, and companies, this paper wants to make sure kids can enjoy the good parts of social media without any worries.

Vehicle Theft Authentication System

This thesis presents a robust security system designed to tackle the escalating issue of vehicle theft and unlicensed driving. By integrating a suite of cutting-edge technologies including Arduino, driver's permit card (DL),radio-frequency ID scanner (RFID),biometric fingerprint sensor (FP), Face Recognition (FR), and Global System for Mobile Communication modem (GSM), the system aims to fortify vehicles against unauthorized access. At its core, Arduino serves as the central intelligence, capable of storing and processing authorized driver data such as facial recognition and fingerprint records. When a driver inserts their license into the RFID reader, the system cross-references the information with stored data. If the license is validated, the system proceeds to authenticate the driver's identity through facial recognition and fingerprint scanning. Successful verification enables the ignition system, granting access to the vehicle. However, any discrepancy triggers an immediate SMS alert via the GSM modem to the vehicle owner, while simultaneously disabling the ignition to thwart unauthorized use. Additionally, an alcohol detection module further enhances safety by preventing vehicle ignition if alcohol is detected in the driver's system. This comprehensive approach not only safeguards vehicles from theft but also promotes responsible driving practices, exemplified by timely license renewal reminders and the enforcement of alcohol-free driving. Key Words: Vehicle security system,Arduino integration,Driver's license card (DL), radio-frequency ID scanner (RFID),Fingerprint module (FP) Face Recognition (FR), GSM-enabled mobile communication device, Unauthorized access prevention,Facial recognition,Fingerprint scanning SMS alerts,Alcohol detection module,Responsible driving practices,License renewal reminders,Alcohol-free driving enforcement.

AI-Enabled Smart Jacket : Revolutionizing Healthcare Monitoring and Environmental Control

The AI smart jacket represents a groundbreaking innovation in healthcare monitoring, merging wearable technology, advanced sensors, and machine learning algorithms. This wearable device integrates GPS and biometric sensors for real-time data capture, facilitating remote access to health metrics via wireless networking. With a focus on user comfort and privacy, the jacket features machine-washable fabrics and robust security measures. Its array of functionalities includes temperature adjustment, SOS capability, oximeter, ECG monitoring, pulse rate tracking, and a fan with massage functions. The jacket's versatility extends from chronic illness management to elderly care, promising to enhance overall well-being and quality of life. By seamlessly integrating technology into daily life, the AI smart jacket stands as a pioneering solution at the forefront of healthcare innovation.

Monitoring Adolescent and Young Adult Patients With Cancer via a Smart T-Shirt: Prospective, Single-Cohort, Mixed Methods Feasibility Study (OncoSmartShirt Study).

Wearables that measure vital parameters can be potential tools for monitoring patients at home during cancer treatment. One type of wearable is a smart T-shirt with embedded sensors. Initially, smart T-shirts were designed to aid athletes in their performance analyses. Recently however, researchers have been investigating the use of smart T-shirts as supportive tools in health care. In general, the knowledge on the use of wearables for symptom monitoring during cancer treatment is limited, and consensus and awareness about compliance or adherence are lacking. The aim of this study was to evaluate adherence to and experiences with using a smart T-shirt for the home monitoring of biometric sensor data among adolescent and young adult patients undergoing cancer treatment during a 2-week period. This study was a prospective, single-cohort, mixed methods feasibility study. The inclusion criteria were patients aged 18 to 39 years and those who were receiving treatment at Copenhagen University Hospital - Rigshospitalet, Denmark. Consenting patients were asked to wear the Chronolife smart T-shirt for a period of 2 weeks. The smart T-shirt had multiple sensors and electrodes, which engendered the following six measurements: electrocardiogram (ECG) measurements, thoracic respiration, abdominal respiration, thoracic impedance, physical activity (steps), and skin temperature. The primary end point was adherence, which was defined as a wear time of >8 hours per day. The patient experience was investigated via individual, semistructured telephone interviews and a paper questionnaire. A total of 10 patients were included. The number of days with wear times of >8 hours during the study period (14 d) varied from 0 to 6 (mean 2 d). Further, 3 patients had a mean wear time of >8 hours during each of their days with data registration. The number of days with any data registration ranged from 0 to 10 (mean 6.4 d). The thematic analysis of interviews pointed to the following three main themes: (1) the smart T-shirt is cool but does not fit patients with cancer, (2) the technology limits the use of the smart T-shirt, and (3) the monitoring of data increases the feeling of safety. Results from the questionnaire showed that the patients generally had confidence in the device. Although the primary end point was not reached, the patients' experiences with using the smart T-shirt resulted in the knowledge that patients acknowledged the need for new technologies that improve supportive cancer care. The patients were positive when asked to wear the smart T-shirt. However, technical and practical challenges in using the device resulted in low adherence. Although wearables might have potential for home monitoring, the present technology is immature for clinical use.

Construction of Athletes' Physical Condition Monitoring and Analysis System Using Biometrics Recognition Technology

Biometrics recognition technology utilizes unique biological characteristics such as fingerprints, iris patterns, facial features, or voiceprints to identify and authenticate individuals. Through advanced algorithms and pattern recognition techniques, biometric systems capture and analyze these physiological or behavioral traits to verify a person's identity. This technology offers high levels of security and accuracy, making it valuable for various applications including access control, time and attendance tracking, border security, and digital payments. The construction of an athletes' physical condition monitoring and analysis system utilizing biometric recognition technology represents a significant advancement in sports science. By integrating cutting-edge biometric sensors and recognition algorithms, this system provides real-time insights into athletes' physiological parameters and performance metrics. Athlete performance monitoring and analysis play pivotal roles in optimizing training strategies, preventing injuries, and enhancing overall athletic performance. In this paper, we propose a novel approach leveraging the Hybrid Multi-Instance Ensemble Classifier (HMIEC) combined with biometric recognition technology to accurately classify athlete data and assess their physical condition. Our study explores the effectiveness of HMIEC across multiple biometric modalities, including ECG-based, fingerprint-based, and facial recognition, in identifying athletes and monitoring their physical parameters. Through a series of experiments and analyses, demonstrate HMIEC's superior classification performance compared to other classifiers, with high accuracy, sensitivity, specificity, and area under the curve (AUC). The reductions in heart rate from 75 to 65 beats per minute, increase in oxygen saturation levels from 98% to 99%, decreases in blood pressure readings from 120/80 mmHg to 110/70 mmHg, and enhancements in flexibility from 30 to 35 centimeters, muscle strength from 100 lbs to 120 lbs, and endurance capacity from 20 to 25 minutes.

How IT technologies are transforming the world of sports for people with disabilities

IT technologies in medical rehabilitation play a key role in opening new perspectives for people with physical disabilities. Sensor devices, virtual reality and specialized software solutions make it possible to create innovative training programs adapted for people with disabilities. Thanks to biometric sensors and monitoring systems, doctors and trainers can more effectively track progress and adjust treatment plans. IT technologies are becoming a powerful tool in the field of medical rehabilitation, opening up new opportunities for a full and active life.

Promoting stress and anxiety recovery in older adults: assessing the therapeutic influence of biophilic green walls and outdoor view.

Previous research has already provided evidence regarding the favorable impact of green walls and outdoor views on stress reduction and anxiety alleviation. However, there has been limited exploration into the combined effects of green walls and outdoor views on older adults. In this study, a between-subjects experiment was conducted using virtual reality (VR) technology with 23 participants. Following exposure to stressors, each participant underwent four randomized sessions, each lasting 5 min, in various virtual reality (VR) environments, encompassing non-biophilic and biophilic environments (including green walls, outdoor views, and their combination). Throughout the process, we measured physiological indicators of stress responses, including heart rate, heart rate variability, skin conductance levels, and blood pressure, using biometric sensors. Psychological changes in participants, including anxiety levels, were evaluated through the State-Trait Anxiety Inventory, recovery scales, and self-reported emotional assessments. In conclusion, in comparison to non-biophilic environments, older adults consistently exhibited lower stress levels, experienced superior anxiety relief, and demonstrated improved recovery in nature connectedness environments, with a notably faster recovery rate. These findings suggest that the incorporation of nature connectedness principles into the indoor environments of public activity spaces within older adults care facilities can significantly contribute to stress reduction and anxiety alleviation among older adults. Furthermore, these effects appear to be contingent on the specific types of nature connectedness environments. These results can provide substantial evidence to support the design of indoor common activity spaces within older adults care facilities.

Modeling Livestock Procedures Toward Precision and Sustainable Livestock Farm in Era of Virtual Technologies: Lessons, Opportunities, Avenues of Digitization

Presently, contemporary and virtual technologies that are being embraced by many businesses have a favorable influence. For instance, biometric sensors of the Internet of Things (IoT), artificial intelligence (AI), big data (BD), blockchain technology (BCT), and so on in livestock permit the real-time observation of the health and behavior of animals. Also, monitoring helps prevent deterioration, diagnose injuries, and stress, and sustain productivity. The collected data from biometric sensors is analyzed by using BD securing the processed information and making it transparent for partners using BCT. Achieving safety and security through collaborating human robots (cobots) Accordingly, these technologies turn traditional livestock into precision livestock farms (PrLFs). Currently, the concept of precision serves as the cornerstone for the advancement of sustainable and user-friendly livestock farm management in many nations as well as globally. Hence, the objective of this study is to embrace the concept of precision and illustrate its influence on livestock farms toward sustainability of livestock farm. These objectives consider catalysts for analyzing and evaluating the sustainability of livestock farms that embrace contemporary technologies in their operations and practices. Moreover, we are constructing an evaluation model for obtaining the most sustainable livestock farm through harassing Multi-Criteria Decision Making (MCDM) techniques where each technique is responsible for a certain function. For instance, CRiteria Importance Through Inter-criteria Correlation (CRITIC) is utilized for obtaining the criteria’s weights and the combined compromise solution (CoCoSo) leverages the obtained weights from CRITIC for ranking the alternatives of livestock farms and recommends the most sustainable and worst alternative. The evaluation for alternatives is performed based on rating four main criteria related to sustainability’s pillars and thirteen sub-criteria. Finally, the utilized MCDM techniques are working under the authority of Triangular Neutrosophic Sets (TrNSs) to bolster these techniques when handling incomplete information and perplexed situations.

INTEGRATING ADVANCED TECHNOLOGIES FOR ENHANCED HSE MANAGEMENT IN THE FMCG SECTOR

The Fast-Moving Consumer Goods (FMCG) sector operates in a dynamic environment, facing numerous challenges in maintaining Health, Safety, and Environment (HSE) standards while meeting the demands of a rapidly evolving market. To address these challenges, integrating advanced technologies has emerged as a strategic approach for enhancing HSE management practices within the FMCG sector. This review explores the integration of various advanced technologies and their impact on improving HSE management in the FMCG industry. The utilization of technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics has revolutionized HSE management practices in the FMCG sector. IoT sensors embedded in production machinery and equipment enable real-time monitoring of environmental conditions, equipment performance, and worker safety. AI-driven predictive analytics algorithms analyze vast amounts of data to identify potential safety hazards, predict equipment failures, and optimize HSE protocols. Furthermore, the adoption of wearable devices equipped with biometric sensors provides continuous health monitoring for employees, ensuring early detection of fatigue, stress, or other health-related issues. Virtual Reality (VR) and Augmented Reality (AR) technologies are utilized for immersive HSE training simulations, enabling employees to practice safety procedures in realistic virtual environments, thus enhancing their preparedness for real-life scenarios. Moreover, the integration of drone technology facilitates remote monitoring of vast operational areas, enabling quick identification of potential hazards and swift response to emergencies. Additionally, blockchain technology ensures the transparency and traceability of HSE data across the supply chain, enhancing accountability and compliance with regulatory standards. The integration of advanced technologies holds significant promise for enhancing HSE management practices in the FMCG sector, fostering a safer and more sustainable operational environment while addressing the evolving challenges of the industry.
 Keywords: HSE, Management, FMCG, Technologies, Technology, Review, Innovation.

Empowering artificial intelligence-based multi-biometric image sensor for human identification

Artificial intelligence (AI) and sensor technology developments have sparked revolutionary shifts in a number of fields, including biometric Identification. In order to improve human identification processes, this research offers a novel method that integrates AI and many biometric image sensors. The accuracy, robustness, and susceptibility to spoofing assaults of conventional single-modal biometric systems are among their many drawbacks. To overcome these challenges, we introduce a secure multi-biometric system that relies on feature-level fusion to identify users. In the preprocessing step, fingerprint images undergo Min-Max normalization to mitigate variations in image quality. In order to extract high-level features from both raw Electrocardiogram (ECG) signals and Min-Max normalized fingerprint images, ResNet50, a deep convolutional neural network, is used. These extracted feature vectors are able to distinguish between the two modalities. We proposed boosted Xgboost as a classifier for authentication in the identification steps to improve performance. The proposed approach is simulated using Python. A comparison study for improved Xgboost is presented using measures for accuracy, precision-recall, and F1-Score. Across all comparative metrics, the technique achieves much better performance. According to experimental findings, the suggested multi-biometric systems are more effective, dependable, and robust than the existing multi-biometric authentication systems.