Search Results (65)

Smart home mobile applications are a primary digital channel through which older adults manage home devices and access daily services. Existing evaluation approaches do not adequately capture the cognitive burden experienced by older users, because dimensional weights are typically assigned through expert judgment rather than derived from target-user data. This study proposes a framework integrating eye-tracking-derived cognitive load with WCAG 2.2 criteria. Evaluation dimensions are defined based on the MOLD-US aging barrier classification, and nine indicators are selected according to compliance level, quantifiability, and relevance to cognitive aging. Cognitive load data from 35 older adults (aged 60–75) were used to calibrate dimensional priorities. Accessibility-related tasks produced significantly higher cognitive load than visual and operational tasks (Cohen’s $d_{\mathrm{z}}$ = 0.855), and the ordering held across three Cognitive Load Index aggregation schemes. A hybrid scoring mechanism combining a multimodal large language model with rule-based scripts was implemented for scalable evaluation. Validation on six high-fidelity prototypes showed strong agreement with expert ratings (Spearman’s $\rho$ = 0.71–0.93) and on the same scoring task, the framework required about 1/14 of the time taken by an expert panel. By calibrating dimensional weights with eye-tracking data from older adults instead of expert judgment alone, the framework integrates WCAG compliance scoring with group-specific priorities, positioned as a design-stage screening tool prior to deployment testing. Full article

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring. Full article

Background/Objectives: The objective assessment of natural tooth mobility remains challenging in clinical practice. This pilot study aimed to investigate the feasibility, repeatability, and agreement of a modified implant stability measurement system adapted for natural teeth using a custom-fabricated titanium bracket and a modified SmartPeg. Methods: Sixteen systemically healthy patients (10 males, six females) and 94 single-rooted permanent teeth with varying mobility grades were included. The tooth mobility was assessed using the Miller Mobility Index, Periotest M, and resonance frequency analysis (RFA) with the Osstell Beacon device. For the Osstell measurements, a custom titanium bracket bonded to the buccal tooth surface allowed for the placement of a modified SmartPeg. Each tooth was measured twice under standardized conditions, and mean values were recorded. The statistical analyses included Spearman correlation analysis, Cohen’s kappa for agreement with Miller categories, and intraclass correlation coefficients (ICCs) to assess the measurement repeatability. Results: The mean Periotest value was 12.70 ± 13.69, and the mean ISQ (implant stability quotient) value was 69.45 ± 19.37. The repeated measurements demonstrated excellent intra-examiner repeatability for both devices (ICC > 0.95). The Periotest values showed substantial agreement with the Miller mobility grades (κ = 0.763; p < 0.001), whereas the Osstell values demonstrated weak agreement with these ordinal categories (κ = 0.094; p = 0.048). A strong negative correlation was observed between the Periotest and Osstell measurements irrespective of the scales (r = −0.865; p < 0.001). Conclusions: In natural dentition, the resonance frequency analysis demonstrated reproducible measurements under controlled experimental conditions and showed measurable associations with conventional mobility assessments. However, the method remains investigational. The findings do not establish clinical validity for the routine assessment of natural tooth mobility. Further studies with larger sample sizes and statistical models accounting for patient-level clustering are required before clinical implementation can be considered. This study is registered at ClinicalTrials.gov (NCT07188168). Full article

This paper presents an indoor self-localization framework for mobile robots, an essential component for automation in Industry 4.0 and smart environments. We evaluate a Received Signal Strength Indicator (RSSI) fingerprinting technique utilizing Long-Range (LoRa) technology to overcome the challenges of congested indoor settings. To optimize communication parameters, the Structural Similarity Index Measure (SSIM) was employed to select the most effective spreading factor, while the entropy of the RSSI database was calculated to verify fingerprint stability. For positional prediction, a Multi-layer Perceptron (MLP) neural network was developed to classify the location of the target within a grid-based experimental setup, featuring cells spaced 60 cm apart. The MLP achieved a validation accuracy of 91.8 percent during training and demonstrated high precision in classifying grid regions within a signal-dense environment. For scenarios where slow-moving robots (5 cm/s) are required, like radiation mapping, this method provide highly accurate high-level localization data.These results suggest that the proposed LoRa-MLP integration provides a robust, low-power solution for high-accuracy indoor positioning systems (IPSs) in modern industrial infrastructure. Full article

Cities are increasingly expected to address digital transformation and sustainability challenges at the same time. However, existing urban indices generally approach smart city and sustainable city perspectives separately, which limits their ability to capture the integrated nature of contemporary urban development. In addition, many index-based studies rely on similar methodological choices. This study develops a composite Sustainable Smart City (SSC) index supported by a systematic scoring framework that brings smartness and sustainability together. The proposed framework follows a step-by-step procedure covering data preparation, normalization, weighting, aggregation, and final scoring. To address information overlap among indicators, a Redundancy-Penalized Entropy Weighting (RPEW) approach is applied. Then, overall SSC scores are calculated using a soft non-compensatory aggregation to emphasize balanced performance across dimensions. The framework is empirically illustrated through a cross-country case study including 38 OECD (Organization for Economic Co-Operation and Development) countries. A machine-learning-based polynomial forecasting approach is used for a limited number of indicators to deal with data gaps allowing the assessment to reflect more up-to-date conditions. The results highlight clear differences in SSC performance and show that strong outcomes in a single dimension are not sufficient to achieve high overall SSC scores. Instead, balanced progress across economic, digital, environmental, governance, mobility, and social dimensions plays an important role. In addition, the proposed framework provides a practical basis for comparative analysis, benchmarking, and policy-oriented evaluation of smart and sustainable urban development. Full article

This study investigates the influence of public transport on the quality of urban life through a combined approach that includes both an objective and a subjective assessment. The objective quality of the public transport network in Timișoara was measured using the Public Transport Accessibility Level (PTAL) index, whose values were recalibrated to better fit the context of an Eastern European post-communist city, while citizens’ perceptions were analyzed based on a public opinion survey in Timișoara, conducted over 5 years on 9490 respondents. The research methods used combine cartography and statistics, with tools such as ArcGIS Pro, IBM SPSS Statistics v27, and R v4.5.2. The results highlight a correlation between accessibility levels and user satisfaction, emphasizing spatial disparities between the city center, which enjoys excellent accessibility, and the periphery, where accessibility is much lower. The integration of these two dimensions provides a holistic perspective on urban mobility and makes relevant contributions to sustainable planning strategies and the development of smart city initiatives. Full article

This study develops a Smart Mobility Index (SMI) tailored to the 17 Local Government Units (LGUs) of Metro Manila to evaluate their readiness to adopt integrated, efficient, and technology-enabled mobility systems. While global smart mobility indices are often ill-suited to the realities of developing countries, this research proposes a context-specific framework built around four thematically grounded dimensions: public transportation service, active mobility, unified cashless fare systems, and smart traffic management. The SMI was constructed through a mixed-method approach combining expert interviews with metropolitan transport specialists and co-occurrence network analysis. The results reveal substantial disparities across LGUs, with central jurisdictions such as Makati, Manila, and Pasay demonstrating significantly higher smart mobility readiness than peripheral LGUs. Clustering identifies three distinct mobility profiles, underscoring persistent structural inequalities in infrastructure, institutional capacity, and digital integration. Forecasts incorporating the completion of six major railway projects by 2035 indicate moderate improvements in overall SMI scores and limited changes in relative rankings, suggesting that infrastructural expansion alone will not reduce regional disparities. Expert insights further highlight both the potential and the constraints of leapfrogging, with interviewees expressing optimism regarding advanced ICT-enabled mobility solutions while acknowledging challenges related to governance fragmentation, limited funding, and uneven technical capabilities. Full article

Smart campuses employ advanced digital technologies and intelligent communication systems to enhance educational, operational, and living environments. This study investigates stakeholder perceptions of smart campus priorities in Saudi Arabia through a structured questionnaire administered to students and faculty. The study considered King Fahd University of Petroleum and Minerals (KFUPM) in Dhahran as a case study in this regard. The survey examined 22 smart campus aspects grouped into six domains: smart education, smart mobility, smart energy and waste management, smart buildings and work environment, smart safety and security, and smart open spaces. The results indicated strong consensus regarding the importance of all domains, with an overall mean rating of 4.3 out of 5.0 and Relative Importance Index (RII) values ranging from 0.77 to 0.91. The highest-ranked aspects included IoT-enabled cooling energy optimization, smart public transportation, smart lighting systems, smart workflow management, e-libraries, and fire prevention and detection systems, reflecting a pronounced emphasis on infrastructure quality, energy efficiency, and operational effectiveness. The findings suggest that smart campus development in Saudi Arabia should prioritize high-impact, user-valued initiatives that align with Vision 2030 objectives including digital transformation. Strategic early investments in smart buildings, energy management, and mobility systems can deliver measurable benefits in this regard. Further research is recommended to consider additional case studies in the Saudi context to ensure that smart campuses remain contextualized and responsive to user needs. Full article

Urban air pollution remains a critical public health issue, especially in densely populated cities where pedestrians experience direct exposure to traffic-related and environmental emissions. This study develops and tests a pollution-aware pedestrian routing framework for Thessaloniki, Greece, designed to minimize environmental exposure while maintaining route efficiency. The framework combines high-resolution air-quality data and computational techniques to represent pollution patterns at pedestrian scale. Air-quality is expressed as a continuous European Air Quality Index (EAQI) and is embedded in a network-based routing engine (OSRM) that balances exposure and distance through a weighted optimization function. Using 3000 randomly sampled origin-destination pairs, exposure-aware routes are compared with conventional shortest-distance paths across short, medium, and long walking trips. Results show that exposure-aware routes reduce cumulative AQI exposure by an average of 4% with only 3% distance increase, while maintaining stable scaling across all route classes. Exposure benefits exceeding 5% are observed for approximately 8% of medium-length routes and 24% of long routes, while short routes present minimal or no detours, but lower exposure benefits. These findings confirm that integrating high-resolution environmental data into pedestrian navigation systems is both feasible and operationally effective, providing a practical foundation for future real-time, pollution-aware mobility services in smart cities. Full article

Smart city initiatives increasingly rely on interoperable and high-quality urban data, yet many cities lack systematic methods for prioritizing which datasets should be developed first. This study proposes an evidence-based framework for smart city data prioritization that integrates data need, data availability, and policy urgency into a unified decision-support model. Using standardized data elements across seven nationally defined smart city domains, the framework was applied to thirty-six certified smart cities in Thailand. Data were collected from municipal authorities and national platforms and structured using ISO-based data element and metadata principles. For each data element, a Need Priority Index, Coverage score, and Policy Readiness indicator were computed to assess governance-relevant data readiness. The results reveal a persistent imbalance between high data demand and low data availability across all domains, with Smart Mobility, Smart Living, Smart Energy, and Smart Economy showing the highest urgency. A Core Common Data Set representing 6.7% of assessed properties was identified, centered on population data, geospatial infrastructure, and plans and performance indicators. The framework provides a scalable approach for guiding investments in interoperable smart city data systems. Full article

The rapid urbanization of recent decades has intensified the need for sustainable and adaptive city models that balance economic growth, environmental protection, and social well-being. This study addresses the challenge of assessing the performance of European smart cities by proposing a composite index of urban sustainability based on citizens’ perceptions. Using data from the Quality of Life in European Cities Survey (2023), the research applies a multi-criteria analytical framework grounded in the Benefit-of-the-Doubt (Data Envelopment Analysis) approach, which allows each city to determine optimal indicator weights and eliminates pre-assigned biases. The analysis integrates six dimensions of smart city performance—mobility, living, environment, economy, governance, and people—to evaluate cities’ adaptability to the needs of their residents. Results reveal that cities such as Aalborg (Denmark), Luxembourg (Luxembourg), Cluj-Napoca (Romania), and Zurich (Switzerland) exhibit the highest performance, demonstrating balanced progress across sustainability-oriented domains. The findings suggest that integrating citizens’ evaluations with data-driven weighting provides a more comprehensive and context-sensitive understanding of urban sustainability. The study concludes that the proposed composite index provides a robust methodological framework for benchmarking European smart cities, supporting policymakers in designing targeted strategies for enhancing livability, inclusiveness, and sustainable urban growth. Full article

Smart city initiatives highlight the vital role of Intelligent Transportation Systems (ITS), which remain underexplored with limited AI-driven solutions integration in real-time urban traffic management across African cities. ITS is crucial to enhance urban mobility efficiency and sustainability to address growing mobility challenges in the era of swift African urbanization. This paper proposes an AI-driven predictive model for the Travel Time Index (TTI), a key metric quantifying urban traffic congestion and mobility performance. Using spatio-temporal analysis, neural networks, and advanced machine learning algorithms, the model processes real-time, multimodal traffic data, capturing congestion patterns, TTI fluctuations, and complex urban travel dynamics, focusing on Casablanca, Morocco, as a smart city case study. Five predictive modeling approaches were carefully selected and rigorously evaluated: Multivariate Linear Regression (MLR), Random Forest (RF), Gradient Boosting, Multilayer Perceptron (MLP), and Support Vector Regression (SVR). Their performance was assessed using standard evaluation metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). All models achieved high accuracy, with Random Forest ranking highest (MAE = 0.315, R² = 0.985). Beyond prediction, the methodology incorporates feature importance analysis and hyperparameter tuning via GridSearchCV to improve operational performance and practical applicability across evolving traffic ecosystems. Hyperparameter optimization further enhanced Random Forest’s accuracy (MAE = 0.220, R² = 0.988). The findings demonstrate improved travel time estimation and congestion management capabilities, offering a scalable, adaptable framework to guide data-driven mobility strategies in diverse urban settings and provide actionable insights for urban planners, policymakers, and mobility stakeholders. Full article

Smart mobility has emerged as a sustainable solution to the challenges of traffic congestion and environmental pollution in cities. Within this concept, Urban Air Mobility (UAM) offers a promising approach to three-dimensional (3D) urban transportation. However, existing UAV path planning studies have primarily focused on obstacle avoidance in low-altitude airspace for small UAVs, with limited consideration of continuous and dynamic risks such as meteorological conditions. As UAM operates at higher altitudes than small UAVs, it is essential to expand the range of flight risks considered in path planning to ensure safe navigation. This study proposes a voxel-based optimal path planning method that integrates multiple flight risks to support various types of UAVs, including those in UAM systems. The proposed method generates a voxel-based flight risk map and extends a two-dimensional (2D) wavefront algorithm into a 3D voxel-based algorithm for deriving optimal paths. Validation through two scenarios, designed in a virtual 3D urban model, demonstrated a 57.59% reduction in the total flight risk index and a 40.72% increase in path length compared with the collision-free path. These results indicate that the proposed method effectively enhances the safety and reliability of UAV navigation in complex urban environments. Full article

Advancements in robotics and computer vision are transforming how infrastructure is monitored and maintained. This paper presents a novel, fully automated pipeline for pavement condition assessment that integrates real-time image analysis with PCI (Pavement Condition Index) computation, which is specifically designed for deployment on mobile and robotic platforms. Unlike traditional methods that rely on costly equipment or manual input, the proposed system uses deep learning-based object detection and ensemble segmentation to identify and measure multiple types of road distress directly from 2D imagery, including surface weathering, a key precursor to pothole formation often overlooked in previous studies. Depth estimation is achieved using a monocular diffusion model, enabling volumetric assessment without specialized sensors. Validated on real-world footage captured by a smartphone, the pipeline demonstrated reliable performance across detection, measurement, and scoring stages. Its potential hardware-agnostic design and modular architecture position it as a practical solution for autonomous inspection by drones or ground robots in future smart infrastructure systems. Full article

This study explores the potential connections between the digital economy and water conservation technologies in the context of China’s water resource consumption from 2008 to 2021. The research employs a state-of-the-art M-MQR technique, including the PCA index, and yields several significant findings. Empirical results reveal that digital technologies play a crucial role in reducing water consumption: Mobile technology decreases water use by −0.00001 to −0.00002 across quantiles, while internet access cuts consumption by −0.0000306 at lower quantiles and −0.0000167 at higher quantiles. The digital economy index shows an overall reduction in water consumption of −0.0537 at lower quantiles and −0.0292 at higher quantiles. Water conservation technologies, such as sprinkler irrigation, also contribute significantly, with reductions of −0.005 at the 10th quantile. Furthermore, water-saving investments show a positive effect on reducing water consumption, with reductions of −0.0105 at the 95th quantile. The study emphasizes that digitalization moderates the impact of water-saving technologies, reducing consumption by −0.0124 to −0.0118 at lower quantiles and −0.00812 to −0.00761 at middle quantiles. These results highlight the potential of digital infrastructure and water-saving investments to improve water use efficiency and address China’s water resource challenges. This study proposes that digital water supply and distribution system devices can help develop smart water infrastructure, reduce waste, and improve efficiency. Full article