Search Results (56)

Cumulative exposure to UV radiation can lead to harmful effects such as skin burns, photoaging, and skin cancer, thus highlighting the importance of using photoprotective formulations. Many sunscreens are vegan and have antioxidant substances to ensure additional photochemoprotective action. We evaluated biophysical, rheological, and sensorial parameters of Face Care Facial Moisturizing Cream^® (P1) and a vegan formulation (P2) by in vitro and in vivo tests. Sun Protection Factor (SPF) was evaluated by Mansur method. Biophysical parameters were analyzed: sebum content, hydration level, transepidermal water loss, erythema and melanin level, skin color, and skin pH. The acceptance profile of the formulations was determined using a 9-point hedonic scale and a 5-point purchase intention test. The SPF values of P1 and P2 obtained by in vitro tests were 25.21 and 12.10, respectively. They also exhibited pseudoplastic and thixotropic behavior, which could contribute to better spreadability and form a protective film. Biometric tests showed an increase in hydration and skin sebum, decreased erythema, and maintenance of skin pH after application of both formulations. The comparison of a commercialized product and a vegan test version showed similar rheological and great acceptance profiles. Therefore, the vegan formulation is a good alternative to reach a different market. Full article

The air we breathe contains contaminants such as particulate matter (PM), carbon dioxide (${\mathrm{CO}}_2$), nitrogen dioxide (${\mathrm{NO}}_2$), and nitric oxide (NO), which, when inhaled, bring about several changes in the autonomous responses of our body. Our previous work showed that we can use the human body as a sensor by making use of autonomous responses (or biometrics), such as changes in electrical activity in the brain, measured via electroencephalogram (EEG) and physiological changes, including skin temperature, galvanic skin response (GSR), and blood oxygen saturation (${\mathrm{SpO}}_2$). These biometrics can be used to estimate pollutants, in particularly ${\mathrm{PM}}_1$ and ${\mathrm{CO}}_2$, with high degree of accuracy using machine learning. Our previous work made use of the Welch method (WM) to obtain a power spectral density (PSD) from the time series of EEG data. In this study, we introduce a novel approach for obtaining a PSD from the EEG time series, developed by Astrapi, called the Astrapi Spectrum Analyzer (ASA). The physiological responses of a participant cycling outdoors were measured using a biometric suite, and ambient ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO were measured simultaneously. We combined physiological responses with the PSD from the EEG time series using both the WM and the ASA to estimate the inhaled concentrations of ${\mathrm{CO}}_2$, ${\mathrm{NO}}_2$, and NO. This work shows that the PSD obtained from the ASA, when combined with other physiological responses, provides much better results (RMSE = 9.28 ppm in an independent test set) in estimating inhaled ${\mathrm{CO}}_2$ compared to making use of the same physiological responses and the PSD obtained by the WM (RMSE = 17.55 ppm in an independent test set). Small improvements were also seen in the estimation of ${\mathrm{NO}}_2$ and NO when using physiological responses and the PSD from the ASA, which can be further confirmed with a large number of dataset. Full article

The field of wearable sensors has evolved with operating devices capable of measuring biomechanics and biometrics, and detecting speech. The transduction, being the conversion of the biosignal to a measurable and quantifiable electrical signal, is governed by a conductive organic polymer. Meanwhile, the conformality of skin to the substrate is quintessential. Both the substrate and the conductive polymer must work in concert to reversibly deform with the user’s movements for motion tracking. While polydimethylsiloxane shows mechanical compliance as a sensor substrate, it is of environmental interest to replace it with sustainable and degradable alternatives. As both the bulk of the weight and area of the sensor consist of the substrate, using renewable and biodegradable materials for its preparation would be an important step toward improving the lifecycle of wearable sensors. This review highlights wearable resistive sensors that are prepared from naturally occurring polymers that are both sustainable and biodegradable. Conductive polythiophenes are also presented, as well as how they are integrated into the biopolymer for sensors showing mechanical compliance with skin. This polymer is highlighted because of its structural conformality, conductivity, and processability, ensuring it fulfils the requirements for its use in sensors without adversely affecting the overall sustainability and biodegradability of resistive sensors. Different sustainable resistive sensors are also presented, and their performance is compared to conventional sensors to illustrate the successful integration of the biosourced polymers into sensors without comprising the desired elasticity and sensitivity to movement. The current state-of-the-art in sustainable resistive sensors is presented, along with knowledge of how biopolymers from different fields can be leveraged in the rational design of the next generation of sustainable sensors that can potentially be composted after their use. Full article

The usage of photoselective anti-hail nets is a modern approach to protect crops from adverse climatic factors with additional beneficial effects on orchard performance. Therefore, this study explored the impact of photoselective nets (blue, red, pearl, and yellow net) and the black net on the microclimate, plant growth, yield, ripening time, and fruit quality attributes of the blueberry cultivar ‘Duke’. The Photosynthetic Photon Flux Density values were elevated under the pearl and yellow nets in both years studied. Average daily air temperatures did not differ between the nets in 2022, while a slight decrease was registered under the black net in 2023. The red net enhanced the average number of younger and total number of shoots per bush and also caused a notable increase in the fruit number and yield per bush, as well as fruit weight, compared to the other tested nets. The pearl net accelerated the onset of ripening in both years studied, while the blue and yellow net delayed ripening in 2022 and 2023, respectively. The blue net was distinguished by the increased blueness of fruit skin and total soluble solids/titratable acidity ratio, while individual sugar types and organic acids were more influenced by the season. The findings indicate that the red net performed the best in terms of most agronomic and biometrical fruit traits of the potted highbush blueberry cultivar ‘Duke’. Full article

This study proposes a biometric authentication method using infrared (IR)-based periocular images captured in virtual reality (VR) environments with head-mounted displays (HMDs). The widespread application of VR technology highlights the growing need for robust user authentication in immersive environments. To address this, the study introduces a novel periocular biometric authentication system optimized for HMD usage. Ensuring reliable authentication in VR environments necessitates overcoming significant challenges, including flicker noise and infrared reflection. Flicker noise, caused by alternating current (AC)-powered lighting, produces banding artifacts in images captured by rolling-shutter cameras, obstructing biometric feature extraction. Additionally, IR reflection generates strong light glare on the iris surface, degrading image quality and negatively impacting the model’s generalization performance and authentication accuracy. This study utilized the AffectiVR dataset, which includes noisy images, to address these challenges. In the preprocessing phase, iris reflections were removed, reducing the Equal Error Rate (EER) from 6.73% to 5.52%. Furthermore, incorporating a Squeeze-and-Excitation (SE) block to mitigate flicker noise and enhance model robustness resulted in a final EER of 6.39%. Although the SE block slightly increased the EER, it significantly improved the model’s ability to suppress noise and focus on critical periocular features, ensuring enhanced robustness in challenging VR environments. Heatmap analysis revealed that the proposed model effectively utilized periocular features, such as the skin around the eyes and eye contours, compared to prior approaches. This study establishes a crucial groundwork for advancing robust biometric authentication systems capable of overcoming noise challenges in next-generation immersive platforms. Full article

Biometrics play an important role in modern access control and security systems. The need of novel biometrics to complement traditional biometrics has been at the forefront of research. The Facial Blood Flow (FBF) biometric trait, recently proposed by our team, is a spatio-temporal representation of facial blood flow, constructed using motion magnification from facial areas where skin is visible. Due to its design and construction, the FBF does not need information from the eyes, nose, or mouth, and, therefore, it yields a versatile biometric of great potential. In this work, we evaluate the effectiveness of novel temporal partitioning and Fast Fourier Transform-based features that capture the temporal evolution of facial blood flow. These new features, along with a “time-distributed” Convolutional Neural Network-based deep learning architecture, are experimentally shown to increase the performance of FBF-based person identification compared to our previous efforts. This study provides further evidence of FBF’s potential for use in biometric identification. Full article

In recent decades, there has been a rising demand for anti-aging interventions aimed at postponing or potentially reversing indicators of skin aging. The use of collagen-based nutraceutical supplements has gained popularity as they have shown promise in enhancing skin health and reducing signs of aging. The aim of this randomized, placebo-controlled, blinded study was to investigate the effects of 2.5 g COLLinstant^® LMW, a novel cosmeceutical containing low-molecular-weight (≤1000 Da) collagen peptides, on skin aging and health. The trial was conducted with 80 healthy women aged 30 years and older. They received a daily oral dose of either the food supplement (n = 40) or placebo (n = 40) for six weeks. Skin assessment was performed based on validated objective methods, such as Visioface 1000D (skin wrinkling), cutometry (elasticity and fatigue), and corneometry (skin hydration) at baseline (T0) and at week 6 (T6). After 6 weeks, participants that received collagen had significant improvements in biometric skin wrinkle parameters from baseline, with a reduction in volume by 46%, in area by 44%, and in depth by 9%, along with a greater increase in skin moisturization (by 34%) than those in the placebo group (p < 0.001). The food supplement did not significantly modify skin firmness or fatigue and had only slight beneficial effects on skin elasticity. The investigational product was well tolerated. The observed effects aligned closely with the subjective assessments reported by study participants. The study provides substantiated evidence supporting the efficacy of low-molecular-weight collagen peptides in restoring altered skin biometric parameters, as objectively assessed. Thus, regular supplementation with this nutraceutical may contribute to achieving smoother and more radiant skin. Full article

Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device’s modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements. Full article

Introduction: Air pollution has numerous impacts on human health on a variety of time scales. Pollutants such as particulate matter—PM₁ and PM_2.5, carbon dioxide (CO₂), nitrogen dioxide (NO₂), and nitric oxide (NO) are exemplars of the wider human exposome. In this study, we adopted a unique approach by utilizing the responses of human autonomic systems to gauge the abundance of pollutants in inhaled air. Objective: To investigate how the human body autonomically responds to inhaled pollutants in microenvironments, including PM₁, PM_2.5, CO₂, NO₂, and NO, on small temporal and spatial scales by making use of biometric observations of the human autonomic response. To test the accuracy in predicting the concentrations of these pollutants using biological measurements of the participants. Methodology: Two experimental approaches having a similar methodology that employs a biometric suite to capture the physiological responses of cyclists were compared, and multiple sensors were used to measure the pollutants in the air surrounding them. Machine learning algorithms were used to estimate the levels of these pollutants and decipher the body’s automatic reactions to them. Results: We observed high precision in predicting PM₁, PM_2.5, and CO₂ using a limited set of biometrics measured from the participants, as indicated with the coefficient of determination (R²) between the estimated and true values of these pollutants of 0.99, 0.96, and 0.98, respectively. Although the predictions for NO₂ and NO were reliable at lower concentrations, which was observed qualitatively, the precision varied throughout the data range. Skin temperature, heart rate, and respiration rate were the common physiological responses that were the most influential in predicting the concentration of these pollutants. Conclusion: Biometric measurements can be used to estimate air quality components such as PM₁, PM_2.5, and CO₂ with high degrees of accuracy and can also be used to decipher the effect of these pollutants on the human body using machine learning techniques. The results for NO₂ and NO suggest a requirement to improve our models with more comprehensive data collection or advanced machine learning techniques to improve the results for these two pollutants. Full article

In this study, an internal fingerprint-guided epidermal thickness of fingertip skin is proposed for optical image encryption based on optical coherence tomography (OCT) combined with U-Net architecture of a convolutional neural network (CNN). The epidermal thickness of fingertip skin is calculated by the distance between the upper and lower boundaries of the epidermal layer in cross-sectional optical coherence tomography (OCT) images, which is segmented using CNN, and the internal fingerprint at the epidermis–dermis junction (DEJ) is extracted based on the maximum intensity projection (MIP) algorithm. The experimental results indicate that the internal fingerprint-guided epidermal thickness is insensitive to pressure due to normal correlation coefficients and the encryption process between epidermal thickness maps of fingertip skin under different pressures. In addition, the result of the numerical simulation demonstrates the feasibility and security of the encryption scheme by structural similarity index matrix (SSIM) analysis between the original image and the recovered image with the correct and error keys decryption, respectively. The robustness is analyzed based on the SSIM value in three aspects: different pressures, noise attacks, and data loss. Key randomness is valid by the gray histograms, and the average correlation coefficients of adjacent pixelated values in three directions and the average entropy were calculated. This study suggests that the epidermal thickness of fingertip skin could be seen as important biometric information for information encryption. Full article

High-resolution electronics on complex curved surfaces have wide applications in fields such as biometric health monitoring, intelligent aircraft skins, conformal displays, and biomimetics. However, current manufacturing processes can only adapt to limited curvature, posing a significant challenge for achieving high-resolution fabrication of electronics on complex curved surfaces. In this study, we propose a novel fabrication strategy that combines electric field-driven jetting and water-assisted transfer printing techniques to achieve the fabrication of high-resolution electronics on complex curved surfaces. The electric field-driven jetting enables the fabrication of high-resolution 2D electronics on sacrificial layer substrates. After dissolving the sacrificial layer, it is observed that the 2D electronics form a self-supporting structure with a certain rigidity and flexibility. During the water-assisted transfer printing process, this self-supporting structure undergoes stretching deformation with excellent conformity of the electronics to curved surfaces while effectively minimizing wrinkles. Finally, we successfully demonstrate the manufacture of 25 μm high-resolution electronics on highly curved surfaces (nautilus shell) and complex (scallop shell, stone) surfaces. The integrity of transferred circuit patterns and consistency of conductors are verified through infrared thermography analysis, confirming the feasibility of this manufacturing strategy. In addition, a protective film with strong adhesive properties is sprayed onto the transferred curved circuits to enhance their adhesion and resistance to extreme environments such as acids and alkalis. Our proposed technique provides a simple and effective new strategy for the fabrication of high-resolution electronics on complex curved surfaces. Full article

Biometric recognition techniques have become more developed recently, especially in security and attendance systems. Biometrics are features attached to the human body that are considered safer and more reliable since they are difficult to imitate or lose. One of the popular biometrics considered in research is palm veins. They are an intrinsic biometric located under the human skin, so they have several advantages when developing verification systems. However, palm vein images obtained based on infrared spectra have several disadvantages, such as nonuniform illumination and low contrast. This study, based on a convolutional neural network (CNN), was conducted on five public datasets from CASIA, Vera, Tongji, PolyU, and PUT, with three parameters: accuracy, AUC, and EER. Our proposed VeinCNN recognition method, called verification scheme with VeinCNN, uses hybrid feature extraction from a discrete wavelet transform (DWT) and histogram of oriented gradient (HOG). It shows promising results in terms of accuracy, AUC, and EER values, especially in the total parameter values. The best result was obtained for the CASIA dataset with 99.85% accuracy, 99.80% AUC, and 0.0083 EER. Full article

Wild edible fruits are an important source for agriculture worldwide suffering from genetic erosion due to a severe genetic diversity reduction and domestication hindrance. In Türkiye, underutilized Prunus spinosa fruits are increasingly being considered as genetic resources and are marginally used by small farmers constituting a real safety valve for the sustainability of the processing plum value chain. Fruits of those plum genotypes differ in their biometric, processing and functional quality attributes. In this study, fruits of eight wild grown blackthorn (Prunus spinosa) genotypes were sampled from the Ispir district of the Erzurum province and subjected to sensory, morphological, biochemical and antioxidant characterization. Aroma, taste and juiciness were used as the criteria for sensory analysis, and a trained panel of ten experts established and evaluated the sensory characteristics of the fruits of the blackthorn. Fruit weight, fruit skin and flesh color as L*, a* and b* values were the main morphological parameters. For biochemical and bioactive analysis, organic acids, SSC (Soluble Solid Content), vitamin C, total anthocyanins, total phenolic content and total antioxidant capacity were determined. Antioxidant capacity was determined by FRAP (ferric reducing antioxidant power) assay. The results indicated significant differences among genotypes for most of the traits. The fruit weight was found between 2.78–3.67 g. The skin L*, a* and b* values were 13.11–16.12, 2.56–3.85 and 2.01–3.44, respectively. The flesh L*, a* and b* values were in the ranges of 17.45–20.37, 4.88–6.73 and 4.12–5.66, respectively. The SSC content ranged from 18.66% to 21.07%. The total phenolic content (TPC), total anthocyanin content (TAC) and ferric reducing antioxidant power (FRAP) were between 372–504 mg GAE/100 g; 53–72 mg cy-3 g eq./100 g and 107–134 mmol Fe (II) eq./g, respectively. The dominant organic acid was malic acid for all genotypes and varied from 1.04 g/100 g to 1.52 g/100 g fresh weight base. The data showed that the analyzed blackthorns, particularly PS-5, PS-3 and PS-2 had bigger fruits indicate their suitability for fresh and dried consumption, PS-1 and PS-3 had higher juiciness, indicating their suitability for processing, and PS-4 and PS-6 had higher human health promoting compounds (higher total phenolic content and antioxidant capacity), making them suitable for future use as functional foods and as promising sources of natural antioxidants. Full article

A challenge in social marketing studies is the cognitive biases in consumers’ conscious and self-reported responses. To help address this concern, biometric techniques have been developed to obtain data from consumers’ implicit and non-verbal responses. A systematic literature review was conducted to explore biometric applications’ role in agri-food marketing to provide an integrated overview of this topic. A total of 55 original research articles and four review articles were identified, classified, and reviewed. It was found that there is a steady growth in the number of studies applying biometric approaches, with eye-tracking being the dominant method used to investigate consumers’ perceptions in the last decade. Most of the studies reviewed were conducted in Europe or the USA. Other biometric techniques used included facial expressions, heart rate, body temperature, and skin conductance. A wide range of scenarios concerning consumers’ purchase and consumption behaviour for agri-food products have been investigated using biometric-based techniques, indicating their broad applicability. Our findings suggest that biometric techniques are expanding for researchers in agri-food marketing, benefiting both academia and industry. Full article

There are many reports that workouts relieve daily stress and are effective in improving mental and physical health. In recent years, there has been a demand for quick and easy methods to analyze and evaluate living organisms using biological information measured from wearable sensors. In this study, we attempted workout detection for one healthy female (40 years old) based on multiple types of biological information, such as the number of steps taken, activity level, and pulse, obtained from a wristband-type wearable sensor using machine learning. Data were recorded intermittently for approximately 64 days and 57 workouts were recorded. Workouts adopted for exercise were yoga and the workout duration was 1 h. We extracted 3416 min of biometric information for each of three categories: workout, awake activities (activities other than workouts), and sleep. Classification was performed using random forest (RF), SVM, and KNN. The detection accuracy of RF and SVM was high, and the recall, precision, and F-score values when using RF were 0.962, 0.963, and 0.963, respectively. The values for SVM were 0.961, 0.962, and 0.962, respectively. In addition, as a result of calculating the importance of the feature values used for detection, sleep state (39.8%), skin temperature (33.3%), and pulse rate (13.2%) accounted for approximately 86.3% of the total. By applying RF or SVM to the biological information obtained from the wearable wristband sensor, workouts could be detected every minute with high accuracy. Full article