Search Results (502)

The recent COVID-19 pandemic has made the public aware of the importance of combating pathogenic microorganisms before they enter the human body. This growing threat from microorganisms prompted us to conduct research into a new type of coating that would be an alternative to the continuous disinfection of touch surfaces. Our goal was to design, synthesise and thoroughly characterise such a coating. In this work, we present a nanocomposite material composed of a thin-layer mesoporous SBA-15 silica matrix containing copper phosphonate groups, which act as catalytic centres responsible for the generation of reactive oxygen species (ROS). In order to verify the structure of the material, including its molecular structure, microscopic observations and Raman spectroscopy were performed. The generation of ROS was confirmed by fluorescence microscopy analysis using a fluorogenic probe. The antimicrobial activity was tested against a wide spectrum of Gram-positive and Gram-negative bacteria, while cytotoxicity was tested on BALB/c3T3 mouse fibroblast cells and HeLa cells. The studies fully confirmed the expected structure of the obtained material, its antimicrobial activity, and the absence of cytotoxicity towards fibroblast cells. The results obtained confirmed the high application potential of the tested nanocomposite coating. Full article

The increasing integration of 3D technologies and machine learning is fundamentally reshaping mineral sciences and cultural heritage, establishing the foundation for an emerging “Mineralogy 4.0” framework. However, public engagement with digital 3D collections is often limited by complex or costly interfaces, such as VR/AR systems and traditional touchscreen kiosks, creating a clear need for more intuitive, accessible, and more engaging and inclusive solutions. This paper presents PETRA, an open-source, gesture-controlled system for exploring 3D rocks and minerals. Developed in the TouchDesigner environment, PETRA utilizes a standard webcam and the MediaPipe framework to translate natural hand movements into real-time manipulation of digital specimens, requiring no specialized hardware. The system provides a customizable, node-based framework for creating touchless, interactive exhibits. Successfully evaluated during a “Long Night of Museums” public event with 550 visitors, direct qualitative observations confirmed high user engagement, rapid instruction-free learnability across diverse age groups, and robust system stability in a continuous-use setting. As a practical case study, PETRA demonstrates that low-cost, webcam-based gesture control is a viable solution for creating accessible and immersive learning experiences. This work offers a significant contribution to the fields of digital mineralogy, human–machine interaction, and cultural heritage by providing a hygienic, scalable, and socially engaging method for interacting with geological collections. This research confirms that as digital archives grow, the development of human-centered interfaces is paramount in unlocking their full scientific and educational potential. Full article

Background: Sexuality is a central aspect of being human, even if people experience it in different ways in various stages of life. Sexuality in older people may be expressed, as well as affection, companionship, touch, and physical contact. However, older peoples’ sexual needs are not properly considered by themselves, caregivers, or healthcare professionals. Reviews on barriers related to identification and satisfaction of sexual needs of people living in nursing home are scarce. In this scenario we intended to summarize the state of evidence regarding sexual need identification and satisfaction among older people living in nursing homes and possible barriers that could limit sexual need identification and satisfaction. Methods: We carried out a narrative review. The included studies responded to the research question, using the following key words: nursing homes, sexuality or sexual need, or sexual behavior, older people. Searched databases included PubMed, Embase, CINAHL, PsycInfo, and Scopus. Results: After searching and screening we included 22 studies, finding three main topics: 1. identification of sexual needs by residents and healthcare personnel attitude and practice; 2. barriers and reasons hindering the identification of sexual needs; and 3. manifestation and satisfaction of sexual needs. Conclusions: The findings showed that nursing homes’ residents have different sexual needs, but there are many organizational, educational, and cultural barriers and negative attitudes of healthcare personnel. Supporting nursing home residents to express their sexual needs is a challenge for the healthcare professionals and managers of nursing homes. Full article

Optical tactile sensing is gaining traction as a foundational technology in collaborative and human-interactive robotics, where reliable touch and pressure feedback are critical. Traditional systems based on total internal reflection (TIR) and frustrated TIR (FTIR) often require complex infrared setups and lack adaptability to curved or flexible surfaces. To overcome these limitations, we developed OptoSkin—a novel tactile platform leveraging direct time-of-flight (ToF) LiDAR principles for robust contact and pressure detection. In this extended study, we systematically evaluate how key optical properties of waveguide materials affect ToF signal behavior and sensing fidelity. We examine a diverse set of materials, characterized by varying light transmission (82–92)%, scattering coefficients (0.02–1.1) cm⁻¹, diffuse reflectance (0.17–7.40)%, and refractive indices 1.398–1.537 at the ToF emitter wavelength of 940 nm. Through systematic evaluation, we demonstrate that controlled light scattering within the material significantly enhances ToF signal quality for both direct touch and near-proximity sensing. These findings underscore the critical role of material selection in designing efficient, low-cost, and geometry-independent optical tactile systems. Full article

Human–computer interaction (HCI) plays a crucial role across various fields, with eye-tracking technology emerging as a key enabler for intuitive and dynamic control in assistive systems like Assistive Robotic Arms (ARAs). By precisely tracking eye movements, this technology allows for more natural user interaction. However, current systems primarily rely on the single gaze-dependent interaction method, which leads to the “Midas Touch” problem. This highlights the need for real-time eye movement classification in dynamic interactions to ensure accurate and efficient control. This paper proposes a novel Gaussian Mixture Model–Hidden Markov Model (GMM-HMM) classification algorithm aimed at overcoming the limitations of traditional methods in dynamic human–robot interactions. By incorporating sum of squared error (SSE)-based feature extraction and hierarchical training, the proposed algorithm achieves a classification accuracy of 94.39%, significantly outperforming existing approaches. Furthermore, it is integrated with a robotic arm system, enabling gaze trajectory-based dynamic path planning, which reduces the average path planning time to 2.97 milliseconds. The experimental results demonstrate the effectiveness of this approach, offering an efficient and intuitive solution for human–robot interaction in dynamic environments. This work provides a robust framework for future assistive robotic systems, improving interaction intuitiveness and efficiency in complex real-world scenarios. Full article

Pluripotent stem cell (PSC)-based therapies are currently in clinical trials. However, one of the main safety concerns includes the potential for cancer formation of the PSC-derived products. Currently, the teratoma in vivo assay is accepted by regulatory agencies for identifying whether PSCs have the potential to become malignant. Yolk sac elements (YSE) are one of the elements that could arise from PSC. Whereas the other malignant element, embryonal carcinoma, is thoroughly studied, this is not the case for YSE. Therefore, more research is needed to assess the nature of YSE. We propose that it is imperative to include the formation of YSE in the safety assessment of PSC due to their close resemblance to the clinical entity of yolk sac tumor (YST), a human malignant germ cell tumor (hGCT). In this review, we extrapolate knowledge from YST to better understand YSE derived from PSC. We demonstrate that both share a similar morphology and that the same characteristic immunohistochemical markers can be used for their identification. We discuss the risk these tumors pose, thereby touching upon genetic abnormalities and gene expression that characterize them, as well as possible disease mechanisms. Integrating the molecular and immunohistochemical markers identified in this review into future research will help to better address the potential malignancy associated with PSC. Full article

Explainable artificial intelligence (XAI) has emerged as a crucial field for understanding and interpreting the decisions of complex machine learning models, particularly deep neural networks. This review presents a structured overview of XAI methodologies, encompassing a diverse range of techniques designed to provide explainability at different levels of abstraction. We cover pixel-level explanation strategies such as saliency maps, perturbation-based methods and gradient-based visualisations, as well as concept-based approaches that align model behaviour with human-understandable semantics. Additionally, we touch upon the relevance of XAI in the context of weakly supervised semantic segmentation. By synthesising recent developments, this paper aims to clarify the landscape of XAI methods and offer insights into their comparative utility and role in fostering trustworthy AI systems. Full article

A product development process establishes requirements not just for the new product’s quality and performance, but also for its manufacturing process, to guarantee that the item is manufactured with minimal impact. This is because, if an issue is discovered after the product has been released, the implications go beyond the expensive cost of the repair; the physical ergonomics problem can affect the worker’s comfort, productivity, and product quality. Virtual reality and digital human modeling are often employed in Industry 4.0 to evaluate ergonomics, but they are rarely used to examine physical ergonomics throughout the product development phases. Our study presents design guidelines to combine virtual reality and digital human modeling to anticipate the physical ergonomics evaluations of the assembly process while the product is still in development. Based on physical observations of body-posture angles and total effort classification, our proof of concept performed comparably to conventional methods. We also observed comparable results when we analyzed attributive factors such as hand clearance and strength. In contrast, our proof of concept has been shown to be limited for occupations involving extra ergonomic physical risk factors, such as touch perception, temperature fluctuations, or size changes. Full article

Touch is a crucial sense for advanced organisms, particularly humans, as it provides essential information about the shape, size, and texture of contacting objects. In robotics and automation, the integration of tactile sensors has become increasingly relevant, enabling devices to properly interact with their environment. This study aimed to develop a biomimetic, skin-inspired tactile sensor device capable of sensing applied force, characterizing it in three dimensions, and determining the point of application. The device was designed as a 4 × 4 matrix of tunneling magnetoresistive sensors, which provide a higher sensitivity in comparison to the ones based on the Hall effect, the current standard in tactile sensors. These detect magnetic field changes along a single axis, wire-bonded to a PCB and encapsulated in epoxy. This sensing array detects the magnetic field from an overlayed magnetorheological elastomer composed of Ecoflex and 5 µm neodymium–iron–boron ferromagnetic particles. Structural integrity tests showed that the device could withstand forces above 100 N, with an epoxy coverage of 0.12 mL per sensor chip. A 3D movement stage equipped with an indenting tip and force sensor was used to collect device data, which was then used to train neural network models to predict the contact location and 3D magnitude of the applied force. The magnitude-sensing model was trained on 31,260 data points, being able to accurately characterize force with a mean absolute error ranging between 0.07 and 0.17 N. The spatial sensitivity model was trained on 171,008 points and achieved a mean absolute error of 0.26 mm when predicting the location of applied force within a sensitive area of 25.5 mm × 25.5 mm using sensors spaced 4.5 mm apart. For points outside the testing range, the mean absolute error was 0.63 mm. Full article

Vibration sensors are important in many engineering fields, including industry, surgery, space, and mechanics, such as for remote and autonomous driving. We propose a novel, cutting-edge vibratory sensor that mimics human tactile receptors, with a configuration different from current sensors such as strain gauges and piezo materials. The basic principle involves the perception of vibration via touch, with a cutaneous mechanoreceptor that is sensitive to vibration. We investigated the characteristics of the proposed vibratory sensor, in which the mechanoreceptor was covered either in hard rubber (such as silicon oil) or soft rubber (such as urethane), for both low- and high-frequency ranges. The fabricated sensor is based on piezoelectricity with a built-in voltage. It senses applied vibration by means of hairs in the sensor and the hardness of the outer cover. We also investigated two proposed parameters: the sensor response time to stimuli to the vibration aiding the equivalent firing rate (e.f.r.) and the gauge factor (GF_,pe) proposed as treated in piezo-resistivity. The evaluation with the parameters was effective in designing a sensor based on piezoelectricity. These parameters were enhanced by the hairs in the sensor and the hardness of the outer cover. Our results were helpful for designing the present novel vibratory sensor. Full article

Human–machine interfaces based on force touch panels have attracted enormous attention due to the merits of the high human–machine interaction efficiency. Many studies have been devoted to diverse force touch technologies. Broad applications in terms of both actual use and research have been developed, such as 3D touch and force-based keystroke authentication. The fruitful results are based on the assumption that users’ touch habits remain unchanged over time; thus, a stationary customized force-sensing model can be built. However, for long-term use, users’ touch habits change due to time-drifting and specific events, causing a decrease in the performance of stationary force-sensing models. To address this issue, a rectified artificial neural network for long-term force sensing in piezoelectric touch panels is presented in this paper. With additional information on the touching time and the occurrence of specific events, the force level predictions were rectified, achieving an accuracy of 97.62% for a long-term data set. The proposed technique enables customized force sensing for long-term use and enhances the human–machine interactive efficiency. Full article

Artificial intelligence (AI) and assistive robotics can transform older-person care by offering new, personalised solutions for an ageing population. This paper outlines recent advances in AI-driven applications and robotic assistance in silver care, emphasising their role in improved healthcare services, quality of life and ageing-in-place and alleviating pressure on healthcare systems. Advances in machine learning, natural language processing and computer vision have enabled more accurate early diagnosis, targeted treatment plans and robust remote monitoring for elderly patients. These innovations support continuous health tracking and timely interventions to improve patient outcomes and extend home-based care. In addition, AI-powered assistive robots with advanced motion control and adaptive response mechanisms are studied to support physical and cognitive health. Among these, companion robots, often enhanced with emotional AI, have shown potential in reducing loneliness and increasing connectedness. The combined goal of these technologies is to offer holistic patient-centred care, which preserves the autonomy and dignity of our seniors. This paper also touches on the technical and ethical challenges of integrating AI/robotics into eldercare, like privacy and accessibility, and alludes to future directions on optimising AI-human interaction, expanding preventive healthcare applications and creating an effective, ethical framework for eldercare in the digital age. Full article

Human–Machine Interfaces (HMIs) in passenger cars have become more complex over the years, with touch screens replacing physical buttons and with layered menu-structures. This can lead to distractions. The purpose of this study is to investigate how often vehicle controls are used while driving and which underlying factors contribute to usage. Thirty drivers were observed during driving a familiar route twice, in their own car and in an unfamiliar car. In a 2 × 1 within-subject design, the experimenter drove along with each participant and used a predefined checklist to record how often participants interacted with specific functions of their vehicle while driving. The results showed that, in the familiar car, direction indicators are the most frequently used controls, followed by adjusting radio volume, moving the sun visor, adjusting temperature and changing wiper speed. Factors that influenced task frequencies included car familiarity, gender, age and weather conditions. The type of car also appears to impact task frequency. Participants interacted less with the unfamiliar car, compared to their own car, which may indicate drivers are regulating their mental load. These results are relevant for vehicle HMI designers to understand which functions should be easily and swiftly available while driving to reduce distraction by the HMI design. Full article

This study introduces a computational framework for understanding the symmetry and asymmetry of human movement by integrating Laban Movement Analysis (LMA). By conceptualizing movement refinement as a structured computational process, we model the golf swing as a series of state transitions where perceptual invariants guide biomechanical optimization. The golf club’s motion is analyzed using the instantaneous screw axis (ISA) and inertia tensor revealing how expert golfers dynamically adjust movement by detecting and responding to invariant biomechanical structures. This approach extends Gibson’s ecological theory by proposing that movement execution follows an iterative optimization process analogous to a Turing machine updating its states. Furthermore, we explore the role of symmetry in motor control by aligning Laban’s X-scale with structured computational transitions, demonstrating how movement coordination emerges from dynamically balanced affordance–action couplings. This insight gained from the study suggests that AI-driven sports training and rehabilitation can leverage symmetry-based computational principles to enhance motion learning and real-time adaptation in virtual and physical environments. Full article

Machine learning (ML) has played an increasingly pivotal role in shaping and evolving artistic expression, leading to new forms of algorithmic creativity. In this study, we explore how ML models, particularly deep learning algorithms such as generative adversarial networks (GANs), have contributed to evolving art styles by learning from vast datasets of historical and contemporary artworks. These algorithms mimic artistic techniques, generate new styles, and even create novel art forms that blend or deviate from traditional artistic boundaries. The challenges of algorithmic creativity, such as concerns about authorship, originality, and the potential loss of human touch in art are also highlighted. The role of machine learning in art raises important philosophical and ethical questions about the nature of creativity and the evolving relationship between human artists and machines. Machine learning has become a powerful tool in expanding the possibilities of artistic expression. While AI-generated art challenges traditional notions of creativity, it also opens up new horizons for collaboration and innovation in art, potentially leading to entirely new art styles in the digital age. Full article