Search Results (622)

Traditional Unmanned Aerial Vehicle (UAV) oblique photogrammetry for 3D real-scene modeling of historic cultural districts suffers from data gaps, insufficient texture, and poor accuracy in complex alleyway environments, hindering the widespread adoption of UAV technology. To address these challenges, this paper establishes a distortion region identification algorithm based on image grayscale variation range parameters. Then, through fusing UAV oblique photogrammetry, close-range smartphone photogrammetry, and Real-Time Kinematic (RTK) positioning technology, it ultimately constructs a 3D real-scene reconstruction technical framework. To validate the method’s effectiveness and reliability, a field experiment was conducted in the Zaoerxiang Historic Cultural District of Zhanggong District, Ganzhou City, Jiangxi Province, China. The experimental results demonstrate that the proposed algorithm can effectively identify distortions in the modeling results from UAV images. After fusing smartphone images from distorted regions and RTK measurements from ground control points (GCPs), the discrepancies in X, Y, and Z coordinates between the results and verification points mostly fall within 10 to 25 mm, while the differences from the measured lengths using a steel tape measure and a leveling rod were within the range of 10 to 20 mm. Furthermore, compared to approaches that rely solely on UAV images or on the fusion of UAV and all ground-based images for modeling, the method proposed in this paper restores building texture information in occluded areas and improves the accuracy of 3D real-scene modeling while simultaneously reducing data-processing and storage requirements and enhancing operational efficiency. It provides a referenceable technical framework for digital preservation, restoration planning, and smart cultural tourism of historic districts. Full article

Background: With the vast and fast-growing number of mHealth applications supporting health, disease management and self-care for older people, the usability of these applications has become a critical factor determining their acceptance and usage. In order to develop mHealth applications suitable for the aging population, it is important to investigate the relationship between older people’s experience with mobile technology in the past, their perception of the usability of mHealth applications and their subsequent use of these applications. Objective: This study investigated the impact of the length of mobile usage on the perceived mHealth application usability of older adults, and the impact of mHealth application usability on the mHealth application user satisfaction and frequency of use of older adults. Methods: This study is based on a cross-sectional survey among older individuals in Al-Ahsa, Saudi Arabia. The measurement model consisted of five distinct constructs with fifteen corresponding indicators including efficiency, learnability, memorability, error handling, and user satisfaction. In terms of analysis, this study included reliability and descriptive statistics as well as correlation and regression analysis, as well as simple and bootstrapped mediation analysis, and, finally, confirmatory factor analysis (CFA) and structural equation modeling (SEM). Based on discriminant validity, the findings suggest that four first-order dimensions, efficiency, learnability, memorability, and error handling, constitute second-order usability dimensions. Results: A total of 271 older adults were included in the final analysis. All constructs demonstrated satisfactory reliability and convergent validity, with Cronbach’s alpha values ranging from 0.797 to 0.862, Composite Reliability values ranging from 0.798 to 0.860, and Average Variance Extracted values ranging from 0.568 to 0.673. Structural equation modeling revealed that mobile usage duration significantly influenced usability (β = 0.616, p < 0.001), usability significantly influenced user satisfaction (β = 0.953, p < 0.001), and user satisfaction significantly influenced use frequency (β = 0.193, p = 0.002). The second-order structural model demonstrated excellent fit to the data (χ²/df = 1.824, CFI = 0.972, TLI = 0.966, GFI = 0.940, AGFI = 0.928, RMSEA = 0.055). Conclusions: Usability plays a central role in explaining the satisfaction of older people with mHealth services and their continuous use of applications. Older people’s experience with their smartphones is associated with their perceptions of the usability of mHealth applications. Higher perceived usability of mHealth applications is positively associated with greater user satisfaction and more frequent use of these applications among older adults. The findings are in line with a usability-centered technology acceptance model. Design of mHealth services should be based on user-centered design principles. In addition to other design principles, efficiency, learnability, memorability, error handling and other usability principles should be particularly addressed in order to increase acceptance of mHealth services by older people. Full article

Smartphone global navigation satellite system (GNSS) positioning is degraded by low-cost antennas, limited receiver hardware, multipath propagation, and noisy code pseudorange observations. Existing correction methods often improve stochastic weighting, estimate coordinate-domain corrections, or smooth receiver trajectories, but they rarely estimate how each satellite contributes to the horizontal position error while preserving line-of-sight (LOS) geometry. This study presents a random-forest-assisted geometry-aware correction method that combines satellite-wise LOS projection error estimation with exponential temporal weighted least squares (Temporal WLS). The horizontal error between the smartphone National Marine Electronics Association (NMEA) solution and the F9P reference position is projected onto each satellite LOS direction and used as the learning target. A random forest model is trained using 26 smartphone GNSS features, including geometry, signal strength, code-derived variation, uncertainty, automatic gain control, and state flags. The predicted LOS errors are fused with satellite geometry through epoch-wise WLS and Temporal WLS. In same-session front-70/back-30 validation, the horizontal root mean square (RMS) error decreased from 2.747 m to 1.033 m. Excluding one suspected non-co-located reference session further reduced the RMS error from 2.867 m to 0.362 m. Full article

Nystagmus, the involuntary rhythmic oscillation of the eyes, is a critical diagnostic marker in vestibular medicine, distinguishing life-threatening central disorders such as stroke from benign peripheral conditions including Benign Paroxysmal Positional Vertigo (BPPV). Despite its clinical importance, accurate nystagmus assessment has long been constrained by expensive infrared video-oculography equipment such as videonystagmography, specialist dependency, and the episodic nature of vestibular symptoms that are often resolved before a clinical encounter. This review synthesizes approximately 50 papers published between 1952 and 2026 across four thematic domains: AI-driven nystagmus analysis, clinical medicine, smartphone and portable hardware innovations, and telemedicine and remote monitoring. On the AI front, classical machine learning models achieve up to 98.77% nystagmus recognition accuracy using ensemble methods, while deep learning frameworks spanning CNNs, U-Nets, LSTMs, and optical flow networks demonstrate clinical-grade slow-phase velocity measurement equivalent to gold standard video-oculography on standard smartphone RGB video. Large language and vision models including GPT-4V and Gemini 2.0 show early-stage promise as zero-shot triage tools but currently fall well below specialist-level diagnostic accuracy. Concurrently, portable hardware innovations ranging from 3D-printed goggle systems to ARKit-based smartphone applications are narrowing the accessibility gap, while telemedicine frameworks enable ictal recording and cloud-based specialist review outside the clinic. Across all domains, the common barriers to clinical translation are dataset scarcity for rare BPPV subtypes, sensitivity to ambient conditions, and the absence of explainable AI mechanisms. This review maps the current state of the field and identifies multimodal data fusion, prospective clinical validation, and interpretable AI as the critical next steps toward equitable, specialist independent vestibular diagnostics. Full article

LGBTQ+ individuals experience disparities in cardiovascular health, but little is known about how daily minority and general stress affect physiological and psychological responses in real-world settings. Twenty LGBTQ+ young adults aged 18–27 completed a 14-day exploratory pilot study using ecological momentary assessment (EMA) with four daily smartphone surveys and continuous smartwatch-based sensor monitoring. This study is among the first to combine EMA with wearable sensor data to capture autonomic stress responses to minority stressors in naturalistic settings. Outcomes included a physiological stress score derived from heart rate variability during the 60 min before each EMA completion, as well as positive and negative affect (PA and NA). Four stress measures, Everyday Discrimination Scale (EDS), Sexual Orientation Microaggression Inventory Short Form (SOMI-SF), EMA of stressful events (EMA-SE), and current perceived stress (CPS), and a combined variable (COMB) were examined. In mixed-effects within-person models, all stress measures showed trends in the expected direction, with higher physiological stress scores, lower PA, and higher NA, though these varied in magnitude and statistical significance. SOMI-SF showed the strongest association with physiological stress, while general stress measures showed stronger associations with affect. These preliminary findings suggest that LGBTQ+-specific and general stressors may differentially engage physiological and psychological response systems; however, caution is warranted given the small sample size. Full article

Background/Objective: Accurate digital recording of implant position is essential for achieving passive fit and predictable outcomes in complete-arch implant-supported prostheses. However, complete-arch cases remain challenging because of increased inter-implant distances, limited anatomical landmarks, soft tissue mobility, scan body-related variables, and cumulative errors during data acquisition and file registration. This narrative review aims to evaluate current intraoral and extraoral digital implant position recording techniques from a clinical decision-making perspective. Methods: A structured narrative literature search was conducted in PubMed from database inception to 15 May 2026 and was supplemented by manual screening of reference lists of key systematic reviews and eligible articles. Systematic reviews, meta-analyses, clinical studies, comparative in vitro studies, dental technique articles, and clinical reports relevant to complete-arch digital implant position recording were considered. Higher-level and clinically relevant evidence was prioritized, whereas technique reports were included primarily for emerging workflows with limited clinical evidence. Results: Intraoral techniques include non-splinted and splinted scan body protocols, calibrated implant scan bodies, calibrated frameworks, and auxiliary reference strategies. These methods may be clinically efficient but remain sensitive to scan path, scanner technology, landmark availability, scan body design, implant distribution, and operator-related factors. Extraoral techniques include stereophotogrammetry, camera- or smartphone-assisted photogrammetric systems, reverse impression workflows, and laboratory scanner-based digitization. These approaches may reduce intraoral stitching errors in complex edentulous arches, but usually require complementary datasets for soft tissue morphology, prosthetic contours, antagonist dentition, and maxillomandibular relationships. Conclusions: Direct intraoral scanner (IOS) protocols may be appropriate in favorable complete-arch situations with accessible scan bodies, limited inter-implant distances, and stable reference geometry. In clinically demanding cases requiring greater cross-arch accuracy, stereophotogrammetry, intraoral photogrammetry, or calibrated scanning approaches may provide more controlled implant position recording. Reverse impression and model-based workflows are particularly useful when a verified interim prosthesis, verification jig, or cast-based reference is available. Regardless of the selected technique, accurate integration of implant coordinates with soft tissue, prosthetic contour, antagonist arch, and occlusal data remains essential. Full article

Objectives: This study used psychological network analysis to examine the interrelationships among smartphone addiction, depression, anxiety, fatigue, sleep quality, and learning engagement in nursing students. Methods: A cross-sectional survey was conducted among 200 nursing students in South Korea using validated self-report instruments. Psychological network analysis was performed using the qgraph and bootnet packages in R. A non-regularized partial correlation network based on Spearman correlations was estimated, and bootstrap was conducted to evaluate the stability and accuracy of network estimates. Results: The strongest positive association was observed between fatigue and depression, whereas smartphone addiction showed the strongest negative association with learning engagement. Depression demonstrated relatively higher centrality within the network, while anxiety showed comparatively lower centrality values. Strength and expected influence estimates demonstrated acceptable stability. Conclusions: The findings suggest meaningful associations among depression, fatigue, sleep quality, smartphone addiction, and learning engagement in nursing students. Learning engagement demonstrated relatively strong connectivity within the network, highlighting its close association with psychological and behavioral factors. These findings support the utility of network analysis for understanding complex interrelationships among psychological variables in nursing students. Full article

Background: Cataract diagnosis and management pose a significant global health challenge, contributing to 17 million cases of blindness and over 83 million cases of vision impairment worldwide in 2020. This issue is particularly acute in regions lacking adequate ophthalmological services, where a shortage of eye care clinicians and specialized equipment like slit-lamp cameras leads to late diagnoses. To address this accessibility gap, we developed a computer-assisted cataract grading system using smartphone-acquired external eye photographs. This approach utilizes image processing and deep learning on a standard, hardware-free smartphone, offering a low-cost and portable alternative to traditional equipment. Methods: The study introduces a new advanced algorithm to stratify cataract severity into three distinct stages: normal, pre-mature, and mature. The methodology was developed using a combined dataset of 799 images sourced from the Cataract v01 Computer Vision Project and the Indian Institute of Technology, Delhi. A key step is isolating the iris and lens using a region of interest (ROI) extraction procedure powered by the open-source MediaPipe framework. Key to the algorithm’s efficacy is the use of transfer learning, adapting four customized ResNet architectures (ResNet-18, ResNet-34, ResNet-50, and ResNet-101) to address medical image analysis intricacies. These models were fine-tuned with specific modifications, including dropout layers and the Adam optimizer, for analyzing the digital periocular images. Results: Evaluation of the models shows varied performance across the various architectures when classifying cataract stages. While the simpler ResNet-18 model exhibited the lowest performance, the deeper models showed significant improvement. The ResNet-50 architecture achieved the highest accuracy of 94%. This model also demonstrated excellent precision (94%), recall (95%), and an F1-score of 95% in multi-class classification, outperforming the other tested models. Its depth enables precise cataract classification, positioning it as a robust and reliable tool for potential medical diagnostic deployment. Conclusions: Deep learning-based analysis of smartphone-acquired external eye images demonstrated feasibility for cataract detection in this study. This method could be a scalable and easy-to-use addition to screening, especially in places where resources are limited. Further work is needed to expand the dataset and to validate the algorithm against established clinical grading systems before broader clinical implementation. Full article

This study was based on a 30 m inside-foot long-pass scenario and aimed to preliminarily evaluate the agreement between MediaPipe Pose, DWPose, YOLO-Pose, and Xsens, as well as their practical utility under real-field conditions. Twelve elite male football players performed 15 consecutive long-passes, with data collected simultaneously using Xsens and two smartphones positioned at 15° and 35° to the right front of the participants. The Intraclass Correlation Coefficient (ICC (2,1)) and Bland–Altman analysis were used to evaluate discrete kinematic measures. Continuous kinematic agreement was assessed using Root Mean Square Error (RMSE) and the Coefficient of Multiple Determination (CMD), while Statistical Parametric Mapping (SPM) and Statistical non-Parametric Mapping (SnPM) compared differences across the entire analysis interval. Across the three models, CMD ranged from 0.13 ± 0.17 to 0.67 ± 0.25, and RMSE ranged from 9.88 ± 8.20° to 39.92 ± 10.44°. The SPM and SnPM results showed that significant differences were mainly concentrated in the bilateral hip, knee, and ankle joints. The three models cannot yet be used for field-based high-precision kinematic data measurement; however, MediaPipe Pose and DWPose may be selectively used for rapid screening of movement patterns and analysis of movement trends in football-specific technical movements. Full article

This study evaluates the feasibility, repeatability, and temporal consistency of a low-cost long-wave infrared (LWIR) thermal imaging workflow for in-vehicle driver monitoring under realistic operating conditions. Two participants were monitored during three independent 60 min driving sessions each. Facial thermal observations were obtained using a consumer-grade mobile LWIR camera operated through a smartphone application environment. Forehead-region temperature data were extracted from a manually positioned region of interest (ROI), including center-point, mean, maximum, and minimum temperature values. Geometric validation was first performed under stationary vehicle conditions in order to confirm forehead-ROI visibility and stability across multiple head orientations and posture variations. Subsequent dynamic sessions were used to evaluate cross-session repeatability and temporal behavior of sampled ROI-based thermal metrics. The results show that the facial thermal patterns remained structurally consistent across repeated sessions, while the sampled temperature trajectories exhibited generally smooth behavior without evidence of progressive within-session instability over the 60 min recordings. Although minor inter-session offsets were observed, normalized analysis confirmed preservation of the relative temporal dynamics. The findings indicate that the examined low-cost LWIR workflow can provide sufficiently stable and repeatable facial thermal observations for feasibility-level driver monitoring analysis under realistic in-vehicle conditions. The contribution of this work lies in a structured validation methodology combining geometric validation, cross-session repeatability, and temporal consistency assessment as a methodological foundation for future thermal-based driver monitoring applications. Full article

With the widespread adoption of smartphones, location-based services have become increasingly important. Consequently, accurate and reliable global satellite navigation system positioning on smartphones has become essential. However, achieving accurate positioning in urban areas remains challenging because of the inherent limitations of smartphones and severe multipath effects. To address this issue, this study proposes two methods to improve positioning accuracy using contextual information. First, an environmental context indicator was used to refine the C/N₀-based observation covariance model. Second, normalized C/N₀ and code-pseudorange residuals were used to detect non-line-of-sight satellites and adjust the observation covariance. Experiments were conducted in both open and urban areas, and performance was evaluated using circular error probable (CEP) and distance root mean square (DRMS). The experimental results showed that, in open areas, the proposed method achieved submeter to decimeter-level horizontal accuracy and precision. In semi-urban areas, CEP95, CEP50, and DRMS decreased by approximately 8, 2, and 4 m, respectively. In urban canyons, CEP95, CEP50, and DRMS decreased by approximately 15, 2, and 5 m, respectively. Full article

Background and Objectives: Adherence to positive airway pressure (PAP) is often suboptimal, and current monitoring relies on device logs that, by design, cannot detect respiratory events outside the therapy window. This creates a physiological blind spot during periods of non-usage. This study aimed to demonstrate the clinical necessity of independent, continuous monitoring using a smartphone-based home sleep apnea test (S-HSAT) by validating treatment effectiveness on adherent nights and quantifying the untreated apnea burden caused by partial adherence. Methods: We prospectively monitored 63 obstructive sleep apnea (OSA) patients commencing PAP therapy. Nightly apnea–hypopnea index (AHI) and usage time were recorded simultaneously by an S-HSAT (ApnoTrack) and the PAP device over a 30-day period. Nights were categorized by the duration discrepancy between S-HSAT and PAP (full-use, ≤5 min; intermediate-use, 5–30 min; partial-use, >30 min) using physiologically and operationally derived thresholds. Results: Final analysis included 39 participants contributing 667 nights (24 participants excluded due to non-use of one or both devices). Full-use nights (46.2%) showed close agreement between S-HSAT and PAP mean AHI (2.8 ± 4.3 vs. 2.5 ± 2.0 events/h; p = 0.13). On intermediate-use and partial-use nights (20.7% and 33.1%, respectively), substantial AHI discrepancies emerged (7.3 ± 5.5 vs. 3.8 ± 3.3 and 11.0 ± 7.4 vs. 2.8 ± 2.5 events/h, respectively; both p < 0.001). Conclusions: Independent S-HSAT monitoring quantified an untreated apnea burden that is invisible to PAP logs alone, while confirming therapeutic efficacy on well-adherent nights. These findings suggest that continuous independent monitoring may help bridge the gap between prescribed therapy and actual physiological outcomes in OSA care. Full article

Rapid localisation of trapped victims after urban disasters is essential but challenging because Bluetooth Low Energy (BLE) beacons are intermittent, radio propagation is obstructed by rubble, UAVs are energy-constrained, and real-world multi-UAV training is impractical in high-risk search-and-rescue (SAR) environments. This study formulates post-disaster victim localisation as a cooperative Dec-POMDP and adapts a model-aided federated multi-agent reinforcement learning framework based on FedQMIX. The proposed pipeline combines a lightweight LoS/NLoS surrogate channel model, PSO-based victim-position estimation, return-to-base and map-feasibility safety checks, an SAR-aligned shaped reward, and a leakage-free centralised training state based on estimated rather than ground-truth victim locations. Each UAV trains locally inside a learned digital-twin simulator and periodically shares only QMIX network parameters, avoiding the exchange of raw trajectories or RSSI logs. The framework is evaluated on two synthetic post-earthquake urban maps representing a compact return-to-base scenario and a larger reach-to-destination scenario. Across five independent seeds per method and map, Model-Aided FedQMIX achieves the highest and most stable victim-localisation performance, with the clearest advantage observed in the larger long-horizon scenario. Additional diagnostic tests examine reward-weight sensitivity, RF channel-shift robustness, BLE/smartphone hardware heterogeneity, non-IID client-data variation, and partial-client FedAvg under missing client updates. The results indicate that combining model-aided localisation cues, decentralised value factorisation, SAR-aligned objective design, and federated parameter sharing can improve the robustness of UAV-based victim-localisation policies. The framework also clarifies deployment considerations for federated SAR coordination, including communication payload, privacy boundaries, heterogeneous client experience, device variability, and intermittent connectivity. This study remains simulation-based, and future validation with real UAVs, BLE devices, and rubble-inspired testbeds is required before operational deployment. Full article

Background: Proprioceptive input from cervical muscles plays a vital role in postural control and coordinated movement. A defect in cervical proprioception, known as joint position error (JPE), is often associated with neck pain. However, the presence of JPE in asymptomatic individuals with varying severities of text neck or forward head posture (FHP) remains underexplored. This study aimed to investigate the presence and correlation of JPE in healthy female university students with different levels of text neck severity. Methods: A cross-sectional study was conducted on 68 female students aged 18–25 years. Participants were categorized into four groups (normal posture, mild, moderate, and severe text neck) through visual observational assessment. JPE was measured in sitting and standing positions using three tools: an inclinometer, a smartphone-based goniometer, and a laser beam. Correlational and comparative analyses were conducted across all groups and measurement tools. Results: The results demonstrated the presence of JPE in all groups, regardless of text neck severity, with no statistically significant differences between them. Additionally, correlation analysis showed no or weak non-significant relationships between JPE and text neck severity across all measurement tools. Conclusions: Cervical JPE may be present in young adults regardless of their text neck posture, and no significant correlation was found between the severity of text neck and proprioceptive deficit. These findings suggest that text neck alone may not be a predictive factor for impaired cervical proprioception in asymptomatic individuals. Early screening remains important, but further research is needed to understand contributing factors and long-term implications. Full article

This paper proposes disturbance detection algorithms to mitigate the oscillations in smartphone camera module actuators induced by external shocks (e.g., drop events). Smartphone camera modules operate under volumetric constraints with inter-component trade-offs. Specifically, the limited space leads to insufficient performance because actuators are unstable under external disturbances. To optimize actuator function, we define the dynamic model of a voice coil motor (VCM) actuator, a controller model, and a shock disturbance model and perform worst-case operational analysis with MATLAB/Simulink (R2015a) simulations. Moreover, we propose two disturbance detection techniques: a phase-based detection algorithm that statistically analyzes the phase difference between the control input and the position feedback signal to detect disturbances and a frequency-based detection algorithm that uses discrete Fourier transform (DFT) to identify the characteristic spectral component of disturbances at 500 Hz. According to the simulation results, both methods reduce recovery time upon disturbance. Furthermore, the frequency-based algorithm achieves faster recovery performance than the phase-based detection algorithm. The phase-based detection method offers low computational complexity but increased processing latency, while the frequency-based detection method requires more memory capacity. The proposed techniques are anticipated to improve the recovery time of smartphone camera modules under disturbances, thereby enhancing system robustness and contributing to a more stable user imaging experience by mitigating image blur. Full article