Search Results (939)

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

Magnitude alone fails to capture the full complexity of pavement roughness; its spatial distribution along a road is equally vital for effective maintenance planning. While traditional assessment has long relied on specialized survey vehicles, the rise of mobile crowdsensing now allows for massive data acquisition via smartphone sensors. This study investigates the spatial structure of pavement roughness using crowdsensed data from the SmartRoadSense platform. Roughness is quantified through the Power of Prediction Error (PPE) indicator derived from smartphone accelerometer signals. The dataset consists of 475 observations sampled at 20 m intervals over approximately 9.5 km of the A3/E45 motorway in southern Italy. A multi-scale spatial–statistical framework is adopted to analyse the roughness signal. The analysis includes the evaluation of scale-dependent statistical descriptors (mean and coefficient of variation), as well as spatial correlation, spectral, and entropy-based measures. The results indicate a short spatial correlation length (approximately 60–100 m) and the absence of a dominant spatial wavelength, suggesting that pavement roughness behaves as a localized multiscale process. A complementary segmentation analysis based on Classification and Regression Trees (CART) is performed to explore the spatial partitioning of the roughness signal. Our analysis indicates that segmentation complexity spikes once the minimum node size drops below roughly 10 observations. This trend points to the existence of localized irregularities that coarser scales simply overlook. Ultimately, these results suggest that mean roughness values alone are insufficient for describing pavement condition and that hybrid spatial–statistical approaches may support more scalable, data-driven, and spatially targeted pavement monitoring strategies for sustainable transportation infrastructure management. Full article

Regulations in Poland require acceptance load tests to verify bridge response under moving loads before structures are approved for operation. These tests are mandatory for new bridges, after major renovations, and for reconstructed structures, and may also be conducted as supplementary assessments of existing bridges to determine their load-carrying capacity. This paper presents one of the first documented applications, to the authors’ knowledge, of low-cost sensing technology for capturing bridge displacements with sub-millimeter tracking precision during acceptance load testing. The study explores the use of modern remote sensing methods based on digital image correlation (DIC) to assess vertical displacements of a truss railway bridge span under moving loads. Video data were recorded using a standard smartphone under nighttime conditions with artificial lighting, demonstrating a highly accessible and cost-effective measurement approach. The collected data were processed using the DES Vision System and compared with results obtained from traditional measurement techniques, such as accelerometers, enabling an evaluation of the accuracy and precision of the DIC method. The findings show that smartphone-based video recordings can provide displacement measurements with millimeter- to sub-millimeter-level tracking precision. Additionally, a numerical finite element method (FEM) model was developed to support interpretation of the structural response under moving loads. Full article

Remote postoperative wound assessment may help improve follow-up after laparoscopic surgery in resource-limited settings. This study evaluated the feasibility and patient satisfaction of smartphone-based postoperative wound assessment following general and bariatric laparoscopic surgery. We conducted a prospective cohort study from June 2022 to June 2023 at a public sector teaching hospital. Consecutive adult patients undergoing elective laparoscopic general or bariatric procedures were invited to participate. Consenting patients submitted wound photographs and clinical queries to their surgeon within 14 days of discharge using an encrypted messaging platform. The primary outcome was patient satisfaction measured using the Patient Satisfaction Questionnaire Short Form (PSQ-18). Secondary outcomes included the proportion of patients requiring escalation to in-person review and the type of remote intervention provided. A total of 113 patients were enrolled. Of these, 21 (18.6%) required escalation to in-person review. Among the 92 patients managed remotely, 52 (46.0%) received reassurance only and 40 (35.4%) required medication prescription or adjustment. The mean PSQ-18 score for the cohort was 79.66 ± 11.24 (range 18–90). Satisfaction was comparable across procedure types. Smartphone-based postoperative wound assessment appears feasible and acceptable in this setting, with most postoperative concerns managed remotely and favourable patient satisfaction. Further controlled studies are needed to assess safety, diagnostic accuracy, and cost-effectiveness. Full article

Smartphone-holder use during motorcycling is increasingly common, but its task-dependent ergonomic effects remain insufficiently understood. This study examined visual, postural, and muscular responses during smartphone-holder use under a simulated riding-posture condition. Forty healthy adults completed five smartphone-use tasks: dynamic viewing, static viewing, texting, seated use, and standing use. Each riding-related task condition lasted one minute, with the final 30 s designated as the stable data collection window. For postural variables, instantaneous values were recorded at four time points (0, 10, 20, and 30 s from the onset of the stable window) and averaged. For electromyography (EMG), integrated EMG (IEMG) was computed over the same 30 s window using ten consecutive non-overlapping 3 s epochs, and averaged for normalization. The neck flexion (NF), upper thoracic angle (UTA), gaze angle (GA), viewing distance (VD), and electromyographic activities of the cervical erector spinae (CES) and upper trapezius (UTZ) were measured using integrated motion-analysis and EMG approaches. Two-way mixed ANOVA and repeated-measures correlation analyses were performed. The task condition significantly affected all measured variables, with effect sizes ranging from moderate to large (all ηp² ≥ 0.155), with texting producing the greatest NF, shortest VD, and highest muscle activation. Strong within-subject associations were identified among visual, postural, and muscular variables across riding-related tasks (VD–NF: r = −0.815, p < 0.001). Females exhibited higher CES and UTZ activation than males. These findings reveal a task-dependent visual–postural–muscular co-variation pattern during scooter-mounted smartphone-holder use and support the application of a sensor-based ergonomic assessment for characterizing task-dependent visual–postural–muscular responses during scooter-mounted smartphone-holder use. Full article

Background/Objectives: Male factor infertility contributes to approximately 40–50% of infertility cases globally, yet traditional laboratory-based semen analysis often imposes logistical and psychological barriers. This study aimed to evaluate the analytical performance and diagnostic concordance of a novel smartphone-based point-of-care testing (POCT) system, Hagobogo Pro, compared with a laboratory-based computer-assisted sperm analysis (CASA) reference system. Methods: This retrospective validation study analyzed 520 video microscopy clips obtained from 104 men undergoing infertility evaluation at a tertiary fertility center. Following World Health Organization (WHO) 2021 guidelines, sperm concentration and total motility were measured using the Hagobogo Pro smartphone device and the reference system. Analytical performance was assessed based on intra-assay precision, operational time, and method agreement using Passing–Bablok regression, Bland–Altman analysis, and Spearman correlation. Results: The smartphone-based system demonstrated strong analytical agreement with the CASA reference, with high correlations observed for sperm concentration (ρ = 0.943) and motility (ρ = 0.7335). Bland–Altman analysis indicated minimal systematic bias, and intra-assay precision showed coefficients of variation below 6%. There were no statistically significant differences in mean parameters between the smartphone device, CASA, and manual assessment. Conclusions: The Hagobogo Pro platform enables rapid, reliable, and standardized sperm concentration and motility quantification, and results showed good agreement with laboratory CASA. While not a replacement for holistic laboratory evaluations, this technology improves access to preliminary male fertility screening and may empower patients by mitigating barriers to initial testing. Full article

This paper explores the feasibility of using smartphones as emergency flight data indication systems in light aircraft. The presented solution may be applied in potential situations such as failures of the vacuum system or the gyroscopes driving analog instruments, as well as electrical power failures in aircraft equipped with digital avionics. Such failures may lead to the loss of essential flight information, significantly increasing pilot workload and conceivably compromising flight safety. The analysis was based on simulations conducted in a computational environment utilizing a custom-developed model. An experimental measurement flight using the MP-02A “Czajka” aircraft was conducted to collect real flight data for integration into a computational model. During the test flight, the aircraft was deliberately maneuvered into various attitudes and flight conditions to evaluate the model’s performance across the widest possible range of operating states. A smartphone mounted in the cockpit recorded sensor data, including accelerometer, gyroscope, magnetometer, and GPS information. The results demonstrated that key flight parameters can be accurately determined using only data recorded by a smartphone. For example, the determined pitch angle values during stall maneuvers deviate from the reference values by no more than 5°. The proposed solution shows significant potential for further development and practical implementation as a supplementary system to assist pilots during in-flight emergencies. Full article

Background: Smartphones are an integral part of young people’s everyday lives and offer an interactive digital environment that can be incorporated into sport training to support engagement and skill development. Methods: A total of 40 male basketball players aged 12–14 years participated in this quasi-experimental study. Participants were allocated by existing school teams, with one team assigned to the experimental group (n = 20) and the other to the control group (n = 20). Both groups completed a six-month training period consisting of three sessions per week. Hand–eye coordination and dribbling-related performance were evaluated using two standardized mobile-application-based field tests with both hands during initial and final assessments. The data were analyzed using mixed-design repeated-measures ANOVA, with time as the within-subject factor and group as the between-subject factor. Results: The mixed-design repeated-measures ANOVA showed significant time × group interactions for all assessed outcomes, indicating greater improvements in coordination performance and bilateral upper-limb performance in the experimental group compared with the control group. Conclusions: These results indicate that mobile-application-based training can be a practical and effective approach for developing coordination and supporting bilateral upper-limb performance in youth basketball players. Full article

Stroke is a leading cause of chronic disability, with heterogeneous sensorimotor impairments that are not well captured by standard clinical assessments. While advanced motion capture and robotic systems provide precise measurements, they are not scalable for widespread clinical use. We developed C-MORE (Computer Vision for Movement Observation and Recovery Enhancement), a smartphone-based framework that uses computer vision and machine learning to automatically score the Box and Blocks Test (BBT) and extract quantitative kinematic metrics. The system combines hand tracking with a custom machine learning (ML) architecture to identify valid block transfers and segment task phases. We evaluated C-MORE in 7 individuals with chronic stroke and a cohort of 10 healthy adults. The system achieved 99.0% agreement with ground-truth scoring, with errors below clinically meaningful thresholds. Kinematic measures derived from the system were sensitive to stroke-related impairments, including reduced movement velocity and increased task duration in affected limbs. Exploratory analyses indicated that grasp-related metrics, particularly the ratio of grasp-to-transfer duration, were correlated with independent measures of proprioception. These findings demonstrate that smartphone-based computer vision can provide accurate, scalable assessment of upper-extremity function. C-MORE offers a practical approach for enhancing clinical evaluation and enabling more precise, individualized rehabilitation strategies. Full article

Tracking biomechanical changes associated with different training modalities remains a methodological challenge in applied sports science. This pilot longitudinal study examined stroke technique stability in seven junior rowers (aged 16.6 ± 0.5 years) across three measurement sessions (March, April, June), separated by two training mesocycles emphasising strength training and intensive rowing, respectively. Upper body angular velocity was recorded using a smartphone-based MEMS sensor fixed to the upper back during incremental ergometer exercise. Overall stroke duration and its standard deviation remained stable throughout the study period, whereas the durations of the two stroke phases corresponding to forward (drive) and backward (recovery) body motion changed systematically across mesocycles. Phase-specific changes were statistically significant in 10 of 12 paired comparisons (rank-sum test) and 7 of 12 within-subject comparisons (Wilcoxon signed-rank test) for phase durations, and in 9 and 5 of 12 comparisons for their standard deviations, respectively. These findings suggest that the internal structure of the rowing stroke is sensitive to training load specificity, even when overall stroke timing remains unchanged, and that smartphone-based angular velocity analysis provides a feasible tool for individualized biomechanical monitoring in young athletes. Full article

Accurate determination of morphometric body measurements is essential for monitoring growth, evaluating production traits, and supporting selection decisions in cattle breeding. However, traditional measurement methods require direct contact with animals, which may increase labor requirements, negatively affect animal welfare, and pose safety risks for operators. This study evaluated the relationship and agreement between traditional tape measurements and smartphone-camera-based morphometric measurements in cattle. A total of 100 cattle raised in the Mediterranean region of Türkiye, including 50 Holstein and 50 Simmental animals, were included in the study. Withers height, body length, rump height, and forechest width were measured using both conventional tools and a smartphone-camera-based method. Regression analyses demonstrated strong linear relationships between methods, particularly for body length and withers height (R² = 0.564–0.961). Bland–Altman analysis revealed small but significant systematic differences between methods, with camera-based measurements generally producing slightly higher values than tape measurements. The strongest agreement was observed for body length measurements, whereas wider limits of agreement were detected for anatomically complex traits, such as rump height and forechest width. Although the findings support the potential applicability of smartphone-based morphometric measurements as a practical and contactless alternative under field conditions, measurements were obtained only from a single lateral view, which should be considered an important methodological limitation. Future studies using multi-view or three-dimensional imaging systems may further improve measurement accuracy and agreement. Full article

Monitoring eccentric hamstring strength is critical for reducing injury risk in soccer players, yet laboratory-based technologies such as isokinetic dynamometry remain costly and impractical for field use. The purpose of this study was to examine the association and exploratory predictive relationship between variables derived from a smartphone application (My Jump Lab) and eccentric hamstring strength outputs obtained with an instrumented field device (NordBord, Vald Performance, Australia), while also quantifying their absolute agreement during the Nordic hamstring exercise (NHE). Thirty-one professional soccer players from a second-division United Arab Emirates team performed the NHE on the NordBord, while a simultaneous two-dimensional (2D) kinematic analysis was conducted using the My Jump Lab app (version 5.0 for iOS; My Jump Lab, Madrid, Spain). Pearson correlations, linear regression models, and Bland–Altman analyses were used to distinguish linear association/prediction from agreement/interchangeability. Results revealed a very large association between My Jump Lab-derived torque estimates and NordBord peak torque (r = 0.77, p < 0.001), with moderate associations for breakpoint angle (r = 0.42–0.43). A combined regression model using My Jump Lab torque and breakpoint angle explained 69.2% of the variance in NordBord torque (SEE = 15.30 N·m), although this predictive result should be interpreted as exploratory because the variables are task-specific and partly share anthropometric and mechanical determinants. Bland–Altman analysis revealed poor agreement, with a large systematic difference and proportional bias, indicating that My Jump Lab overestimated torque values at higher strength levels (mean bias = +511.9 N·m). Therefore, torque values derived from the app should be interpreted as relative indicators rather than absolute equivalents to instrumented measurements. From a practical perspective, My Jump Lab may offer a low-cost option for broad screening or relative group profiling when instrumented devices are unavailable, but it should not be used as a substitute for instrumented devices or for individual longitudinal monitoring based on absolute torque values. Full article

Smartphone colorimetry has emerged as a low-cost and accessible approach for participatory environmental monitoring. In this feasibility study, mangrove soil samples collected at two depths (approximately 0 and 30 cm) and three distances from the shoreline (−10, 0, and 10 m) were analyzed using smartphone colorimetry. The redness (a*) and yellowness (b*) tended to decrease from the seaward side toward the landward side. The lightness (L*) showed a strong agreement with measurements obtained from a standard spectrophotometer, whereas systematic deviations were observed for chromatic coordinates, with underestimation of a* and overestimation of b* by the smartphone measurements. Soil colors were further examined alongside mineral composition determined by X-ray fluorescence (XRF) and organic matter characteristics obtained from thermogravimetric analysis (TGA). No systematic relationships were identified between color parameters and mineral composition or organic matter weight loss, highlighting the complex and multi-factorial nature of mangrove soil color. Although wetting generally reduced L* and b* values, the responses to increasing water content were not monotonic. These findings indicate that smartphone colorimetry is effective for capturing relative variations in soil lightness under controlled conditions, while emphasizing the need for calibration and cautious interpretation. The accessibility of smartphone-based measurements also suggests potential in public engagement. Full article

Wearable sensors are transformative tools for continuous gait assessment in daily life. Tripping, a leading cause of falls, is closely linked to inadequate foot clearance, making accurate foot height measurement critical for fall risk evaluation. Inertial measurement units offer a practical solution for foot trajectory reconstruction; however, conventional drift correction methods such as zero-velocity updates fail to adequately address cumulative height errors. Recent kinematic constraint-based approaches improve height accuracy but remain limited to offline processing and lack simultaneous activity classification. To address these gaps, we developed a real-time, single-IMU system for continuous foot height trajectory reconstruction with simultaneous classification of five locomotion activities deployed on a smartphone. Twenty healthy adults were recruited for model training and independent validation. Level walking maintained ground reference ($0.0$ cm, 95% CI: [$-1.8$, $1.8$] cm), cumulative height errors remained below 1.1 cm across ramp and stair negotiation with a mean absolute error of 0.42%, and obstacle clearance was quantified. The system achieved 96.08% overall classification accuracy with less than one gait cycle latency. Toe height was estimated through rigid-body transformation with comparable accuracy to the foot height. This framework provides a practical foundation for real-time gait intervention and fall prevention applications. Full article

Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized ‘Yotsuboshi’ strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model’s classifications of ‘NormalN’ and ‘LowN’ statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions. Full article