Search Results (2,635)

Dementia is a progressive condition that affects cognition, communication, mobility, and independence, posing growing challenges for individuals, caregivers, and healthcare systems. While traditional care models often focus on symptom management in later stages, emerging artificial intelligence (AI) technologies offer new opportunities for proactive and personalized support across the dementia trajectory. This concept paper presents the Assistive Intelligence framework, which aligns AI-powered interventions with each stage of dementia: preclinical, mild, moderate, and severe. These are mapped across four core domains: cognition, mental health, physical health and independence, and caregiver support. We illustrate how AI applications, including generative AI, natural language processing, and sensor-based monitoring, can enable early detection, cognitive stimulation, emotional support, safe daily functioning, and reduced caregiver burden. The paper also addresses critical implementation considerations such as interoperability, usability, and scalability, and examines ethical challenges related to privacy, fairness, and explainability. We propose a research and innovation roadmap to guide the responsible development, validation, and dissemination of AI technologies that are adaptive, inclusive, and centered on individual well-being. By advancing this framework, we aim to promote equitable and person-centered dementia care that evolves with individuals’ changing needs. Full article

Introduction: Rare diseases disperse expertise across institutions and borders, making structured second-opinion systems a pragmatic way to concentrate subspecialty knowledge and reduce diagnostic delays. This scoping review mapped the design, governance, adoption, and impacts of such services across implementation scales. Objectives: To describe how second-opinion services for rare diseases are organized and governed, to characterize technological and workflow models, to summarize benefits and barriers, and to identify priority evidence gaps for implementation. Methods: Using a population–concept–context approach, we included peer-reviewed studies describing implemented second-opinion systems for rare diseases and excluded isolated case reports, purely conceptual proposals, and work outside this focus. Searches in August 2025 covered PubMed/MEDLINE, Scopus, Web of Science Core Collection, Cochrane Library, IEEE Xplore, ACM Digital Library, and LILACS without date limits and were restricted to English, Portuguese, or Spanish. Two reviewers screened independently, and the data were charted with a standardized, piloted form. No formal critical appraisal was undertaken, and the synthesis was descriptive. Results: Initiatives were clustered by scale (European networks, national programs, regional systems, international collaborations) and favored hybrid models over asynchronous and synchronous ones. Across settings, services shared reproducible workflows and provided faster access to expertise, quicker decision-making, and more frequent clarification of care plans. These improvements were enabled by transparent governance and dedicated support but were constrained by platform complexity, the effort required to assemble panels, uneven incentives, interoperability gaps, and medico-legal uncertainty. Conclusions: Systematized second-opinion services for rare diseases are feasible and clinically relevant. Progress hinges on usability, aligned incentives, and pragmatic interoperability, advancing from registries toward bidirectional electronic health record connections, alongside prospective evaluations of outcomes, equity, experience, effectiveness, and costs. Full article

While electroencephalography (EEG) is effective for assessing stress and mental workload, its widespread adoption is currently hindered by the complex setup of most existing EEG systems. This article presents a new in-ear EEG system and investigates its feasibility for developing robust models to quantify stress and mental workload levels. The system consists of a single-channel EEG acquisition device that has a similar form factor as user-generic earpieces. All electrodes including passive, reference and bias electrodes were put on the ear, which optimized the device’s usability. We validated the system through two experiments with 66 subjects to collect EEG data under varying stress and mental workload conditions. We developed classification and regression models to predict stress and mental workload levels from the data. Cross-subject stress classification achieved 77% accuracy, while within-subject stress regression yielded an average R2 of 0.76 ± 0.20. Two-class mental workload level classification reached accuracies between 70% and 80% for the arithmetic and finger tapping tasks. Feature importance analysis revealed that frequency-domain EEG features, particularly in the alpha and beta bands, significantly contributed to the models’ performance. However, we observed lower within-subject feature variation and model accuracy for the mental rotation, potentially due to the distance between brain regions engaged and the device’s recording site. Our findings demonstrate the potential of using the presented EEG device to monitor stress and mental workload in real-time. Full article

This paper presents an Interoperable User-Centred Digital Twin (I-UCDT) framework for sustainable energy system management, addressing the growing complexity of energy generation, storage, demand, and grid interaction across industrial and community-scale systems. The proposed framework provides a unified environment for the visual representation and management of interconnected energy components, supporting informed decision-making among diverse stakeholder groups. The I-UCDT framework adopts a modular plug-and-play architecture based on the Functional Mock-up Interface (FMI) standard, enabling scalable and interoperable integration of heterogeneous energy models from platforms such as Modelica, MATLAB/Simulink, and EnergyPlus. A standardised data layer processes and structures raw model inputs, while an interactive visualisation layer translates complex energy flows into intuitive, user-accessible insights. By applying human–computer interaction principles, the framework reduces cognitive load and enables users with varying technical backgrounds to explore supply–demand balancing, decarbonisation pathways, and optimisation strategies. It supports the full lifecycle of energy system design, planning, and operation, offering flexibility for both industrial and community-scale applications. A case study demonstrates the framework’s potential to enhance transparency, usability, and energy efficiency. Overall, this work advances digital twin research for energy systems by combining technical interoperability with explicitly formalised user-centred design characteristics (C1–C10) to promote flexible and sustainable energy system management. Full article

Background/Objectives: An individual with a hand disability, whether caused by an accident, disease, or congenital condition, may have significant problems with their daily activities, self-perception, and ability to work. Prosthetic hands can be used to restore essential hand functions, and features such as adaptive grasps can enhance their usability. Due to noise in the sEMG signal and hardware limitations in the system, reliable myoelectric control remains a challenge for low-cost prosthetics. ESP32 microcontrollers are used in this study to develop an SVM-based sEMG classifier that addresses these issues and improves responsiveness and accuracy. A 3D-printed mechanical structure supports the prosthesis, reducing production costs and making it more accessible. Methods: The prosthetic hand is developed using an ESP32 as the microcontroller, a Myoware Muscle Sensor to detect muscle activity, and an ESP32-based control system that integrates sEMG acquisition, SVM classification, and finger actuation with FSR feedback. A surface electromyography (sEMG) method is paired with a Support Vector Machine (SVM) algorithm to help classify signals from the sensor to improve the user’s experience and finger adaptability. Results: The SVM classifier achieved 89.10% accuracy, an F1-score of 0.89, and an AUC of 0.92, with real-time testing demonstrating that the ESP32 could reliably distinguish flexion and extension signals and actuate the servo, accordingly, producing movements consistent with the kinematic simulations. Complementing this control performance, the prosthetic hand was constructed using a coupled 4 bar linkage mechanism fabricated in PLA+, selected for its superior factor of safety compared to the other tested materials, ensuring sufficient structural reliability during operation. Conclusions: The results demonstrate that SVM-based sEMG classification can be effectively implemented on low-power microcontrollers for intuitive, low-cost prosthetic control. Further work is needed to expand beyond two-class detection and increase robustness against muscle fatigue and sensor placement variability. Full article

This study presents a field-oriented AI web system for durian orchard management that recognizes leaf health from on-orchard images under variable illumination. Two complementary pipelines are employed: (1) a rule-based module operating in HSV and CIE Lab color spaces that suppresses sun-induced specular highlights via V/L* thresholds and applies interpretable hue–chromaticity rules with spatial constraints; and (2) a Deep Feature (PCA–SVM) pipeline that extracts features from pretrained ResNet50 and DenseNet201 models, performs dimensionality reduction using Principal Component Analysis, and classifies samples into three agronomic classes: healthy, leaf-spot, and leaf-blight. This hybrid architecture enhances transparency for growers while remaining robust to illumination variations and background clutter typical of on-farm imaging. Preliminary on-farm experiments under real-world field conditions achieved approximately 80% classification accuracy, whereas controlled evaluations using curated test sets showed substantially higher performance for the Deep Features and Ensemble model, with accuracy reaching 0.97–0.99. The web interface supports near-real-time image uploads, annotated visual overlays, and Thai-language outputs. Usability testing with thirty participants indicated very high satisfaction (mean 4.83, SD 0.34). The proposed system serves as both an instructional demonstrator for explainable AI-based image analysis and a practical decision-support tool for digital horticultural monitoring. Full article

Crowdsensing data serves as a crucial resource for supporting spatiotemporal applications and services. However, its inherent heterogeneity and quality uncertainty present significant challenges for data usability assessment: the evaluation methods are difficult to standardize due to the diverse types of data; assessment dimensions are predominantly confined to internal quality attributes; and a comprehensive framework for data usability evaluation remains lacking. To address these challenges, this study proposes an innovative, multi-layered usability assessment framework applicable to six major categories of crowdsensing data: specialized spatial data, Internet of Things (IoT) sensing data, trajectory data, geographic semantic web, scientific literature, and web texts. Building upon a systematic review of existing research on data quality and usability, our framework conducts a comprehensive evaluation of data efficiency, effectiveness, and satisfaction from dual perspectives—data sources and content. We present a complete system comprising primary and secondary indicators and elaborate on their computation and aggregation methods. Indicator weights are determined through the Analytic Hierarchy Process (AHP) and expert consultations, with sensitivity analysis performed to validate the robustness of the framework. The practical applicability of the framework is demonstrated through a case study of constructing a spatiotemporal knowledge graph, where we assess all six types of data. The results indicate that the framework generates distinguishable usability scores and provides actionable insights for improvement. This framework offers a universal standard for selecting high-quality data in complex decision-making scenarios and facilitates the development of reliable spatiotemporal knowledge services. Full article

Background: Effectively managing pain in adults remains challenging, particularly in individuals with cognitive impairment or communication difficulties. Digital technologies, including artificial intelligence (AI)-enabled facial recognition and mobile applications, are emerging as innovative tools to improve the objectivity and consistency of pain evaluation. This scoping review aimed to map the current evidence on digital pain-assessment tools used with adult and older populations, focusing on validity, reliability, usability, and contributions to person-centred care. Methods: The review followed the Joanna Briggs Institute methodology and Arksey and O’Malley framework and was reported in accordance with PRISMA-ScR guidelines. Systematic searches were conducted in PubMed, CINAHL Complete, Medline (ALL), and PsycINFO for English-language studies published from 2010 onwards. Eligible studies included adults (≥18 years) using digital tools for pain assessment. Data extraction and synthesis were performed using Covidence, and findings were analyzed thematically. Results: Of 1160 records screened, ten studies met inclusion criteria. Most research was quantitative and conducted in high-income clinical settings. Five tools were identified: ePAT/PainChek^®, Painimation, PainCAS, Pain Clinical Assessment System, and Active Appearance Model. Four key themes emerged: (1) Validity and Reliability of Digital Pain Assessment Tools; (2) Comprehensive Pain Evaluation Across Contexts (Rest vs. Movement); (3) Usability and Integration into Clinical Practice; (4) Enabling Person-Centred Pain Management and Future Directions. Conclusions: Emerging evidence suggests that facial-recognition-based digital pain-assessment tools may demonstrate acceptable psychometric performance and usability within dementia care settings in high-income countries. However, evidence relating to broader adult populations, diverse care contexts, and low-resource settings remains limited, highlighting important gaps for future research. Full article

Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article

The transition toward decentralized energy systems has amplified interest in peer-to-peer electricity trading. However, research on prosumer behavior in such markets remains fragmented, hindered by a lack of benchmarkable experimental infrastructure. Addressing this gap, the LabChain system was developed—a modular, interactive prototype designed to study human behavior in synthetic P2P electricity markets under controlled laboratory conditions. This system integrates real-world technologies, such as blockchain-based transaction backends, flexibility market interfaces, and asset control tools, allowing fine-grained observation of strategic and perceptual dimensions of prosumer activity. The research followed an iterative design approach to develop the infrastructure for experimental energy economics research, and to assess its effectiveness in aligning participant experience with design intentions. Based on the meta-requirements generality, affordance-centric design, and technological grounding, 13 detailed peer-to-peer market, software, and system requirements that allow for system evaluation were developed. As a proof of concept, seven participants simulated prosumer behavior over a week through interaction with the system. Their interaction with the system was analyzed through simulation data and focus group interviews, using a modified thematic content analysis with a hybrid inductive–deductive coding approach. The main achievements are (i) the design and implementation of the LabChain system as a modular infrastructure for P2P electricity market experiments, (ii) the development of an associated experimental workflow and research design, and (iii) its demonstration through an illustrative, proof-of-concept evaluation based on thematic content analysis of a single focus group session focusing on interaction and perceptions. The behavioral results from an initial session are limited, exploratory, and demonstrative in nature and should be interpreted as illustrative only. They nevertheless revealed tension between system flexibility and cognitive usability: while the system supports diverse strategies and market roles, limitations in interface clarity and information feedback constrain strategic engagement. Full article

This study develops an immersive marine Information and Communication Technology (ICT) convergence framework designed to enhance coastal climate resilience by improving accessibility, visualization, and communication of scientific research on Dokdo (Dok Island) in the East Sea. High-resolution spatial datasets, multi-source marine observations, underwater imagery, and validated research outputs were integrated into an interactive virtual-reality (VR) and web-based three-dimensional (3D) platform that translates complex geophysical and ecological information into intuitive experiential formats. A geospatially accurate 3D virtual model of Dokdo was constructed from maritime and underwater spatial data and coupled with immersive VR scenarios depicting sea-level variability, coastal morphology, wave exposure, and ecological characteristics. To evaluate practical usability and pro environmental public engagement, a three-phase field survey (n = 174) and a System Usability Scale (SUS) assessment (n = 42) were conducted. The results indicate high satisfaction (88.5%), strong willingness to re-engage (97.1%), and excellent usability (mean SUS score = 80.18), demonstrating the effectiveness of immersive content for environmental education and science communication crucial for achieving Sustainable Development Goal 14 targets. The proposed platform supports stakeholder engagement, affective learning, early climate risk perception, conservation planning, and multidisciplinary science–policy dialogue. In addition, it establishes a foundation for a digital twin system capable of integrating real-time ecological sensor data for environmental monitoring and scenario-based simulation. Overall, this integrated ICT-driven framework provides a transferable model for visualizing marine research outputs, enhancing public understanding of coastal change, and supporting sustainable and adaptive decision-making in small island and coastal regions. Full article

Although DNS has been continuously extended to improve usability and security, its centralized, server-based architecture leaves fundamental limitations unresolved, including single points of failure (SPOF), susceptibility to censorship, and exposure to DDoS. This study examines blockchain-based DNS (BDNS) as an alternative proposed to mitigate these structural issues. We first synthesize prior research and systems on BDNS, and then conduct a comparative analysis using practical deployability as the primary criterion. Specifically, we selected seven representative BDNS projects, including Namecoin, Handshake, and Ethereum Name Service (ENS), and evaluated them under a common set of criteria: (i) the record model, finality, and TTL semantics; (ii) friction along real resolution paths involving resolvers, browsers, and gateways; and (iii) interoperability with the legacy DNS, including DNSSEC and DNS over TLS(DoT)/DNS over HTTPS(DoH), together with migration scenarios. The analysis indicates that many systems rely on gateways and client-side extensions, limiting native resolution paths. It further finds that finality latency, dependence on off-chain indexing and availability, and the interplay of key management and tokenomics design increase operational complexity and raise barriers to adoption. Building on these findings, the paper derives operational requirements and proposes a coexistence-first, five-layer migration framework that incrementally integrates BDNS while retaining the legacy DNS. This provides an incremental path toward a more resilient, inclusive, and secure global naming infrastructure. Full article

Precisely assessing electric vehicle hosting capacity (EVHC) is critical for ensuring the secure integration of EVs and optimizing the use of distribution network resources. Although optimization-based methods such as Particle Swarm Optimization (PSO) can identify a high theoretical HC under steady-state voltage constraints, these static formulations fail to capture short-term dynamics such as photovoltaic (PV) intermittency and uncoordinated EV arrivals. As a result, the hosting capacity that can actually be used in practice is often reduced to a much lower capacity to keep the system operating safely. This study compares optimization-based and simulation-based HC assessments and introduces a Weighted Average Power Estimator (WAPE)-based dynamic control framework to preserve the higher HC identified by optimization under real-world conditions. Case studies on a modified IEEE 13-bus system show PV drops of 90% during a 4-s cloud event. Studies also demonstrate that a sudden clustering of multiple EVs would significantly lower effective HC. With WAPE control, the system maintains stable operation at full HC, holding the bus voltage within an acceptable range (400–430 V) during the two events, representing a 2–3% voltage improvement. In addition, WAPE allows the EV to continue charging at a lower rate during disturbances, reducing the total charging time by almost 10% compared with completely stopping the charging process. Overall, the proposed WAPE substantially improves the usable and sustainable HC of distribution networks, ensuring reliable EV integration under dynamic and uncertain operating conditions. Full article

Amidst the surge of high-density urban development and the growing demand for high-quality spaces, the Usable Area Coefficient (UAC) has emerged as a pivotal metric in the architectural planning. The rational calibration of the UAC for primary school buildings is key to balancing intensive land use, educational demands, and the well-being of children. Taking primary schools in a district of Qingdao as the research subject, this research rationally optimizes the range of UAC by constructing an evolutionary game model, based on quantitatively analyzing the divergent perspectives and requirements of three stakeholders: the government, school administrators, and students. After further identifying the key factors that influence the ultimate decision, the study yields the following insights: (1) The incremental comprehensive benefit emerges as the linchpin influencing the UAC. (2) The government’s risk compensation to schools and the benefit-sharing coefficient between schools and students exert significant impacts on system evolution. (3) Effective control of construction and land costs, coupled with enhanced availability of open activity spaces, paves the way for consensus on low UAC. This research not only furnishes a theoretical framework and practical guidance for harmonizing land use efficiency with educational excellence but also steers the design of salubrious primary school environments and informs pertinent policy-making. Full article

Background/Objectives: Home care plays a crucial role in contemporary healthcare systems, particularly in the long-term care of people with chronic and progressive illnesses. Family caregivers often experience substantial physical, emotional, and organizational burden. Telemedicine and digital health applications have the potential to support home care by improving health monitoring, communication, and care coordination. However, their use among family caregivers remains inconsistent, and little is known about how organizational support structures such as telemedicine centers influence acceptance and everyday use. This study aims to examine the benefits of telemedicine in home care and to evaluate the role of telemedicine centers as supportive infrastructures for family caregivers. Methods: A mixed-methods design was applied. Quantitative data were collected through an online survey of 58 family caregivers to assess the use of telemedicine and digital health applications, perceived benefits, barriers, and support needs. This was complemented by an in-depth qualitative case study exploring everyday caregiving experiences with telemedicine technologies and telemedicine center support. A systematic literature review informed the theoretical framework and the development of the empirical instruments. Results: Most respondents reported not using telemedicine or digital health applications in home care. Among users, telemedicine was associated with perceived improvements in quality of care, particularly through enhanced health monitoring, improved communication with healthcare professionals, and increased feelings of safety and control. Key barriers to adoption included technical complexity, data protection concerns, and limited digital literacy. Both quantitative findings and the qualitative case study highlighted the importance of structured support. Telemedicine centers were perceived as highly beneficial, providing technical assistance, training, coordination, and ongoing guidance that facilitated technology acceptance and sustained use. Conclusions: Telemedicine and digital health applications can meaningfully support home care and reduce caregiver burden when they are embedded in supportive socio-technical structures. Telemedicine centers can function as central points of contact that enhance usability, trust, and continuity of care. The findings suggest that successful implementation of telemedicine in home care requires not only technological solutions but also accessible organizational support and targeted training for family caregivers. Full article