Search Results (569)

Cervical range of motion (CROM) has been used in research and clinically for assessing cervical health. Gold-standard goniometers tend to be cumbersome. However, Inertial Measurement Units (IMUs) or vision-based alternatives demand frequent calibration and/or costly hardware; moreover, the subject is aware of being measured and there is a risk of breaching privacy. In response, we have developed a non-contact HPNet system for head pose estimation (HPE) that can use a rear-facing camera to quantify CROM accurately. A Re-parameterized Visual Geometry Group (RepVGG)-D2se model is employed as the backbone of the network, and a Spatial Feature Enhancement (SCFE) module is incorporated to improve feature extraction. HPNet was evaluated on the large-scale Carnegie Mellon University (CMU) Panoptic dataset, achieving a mean absolute error (MAE) of 3.48°, 3.22°and 3.34° for yaw, pitch and roll respectively. Inter-instrument reliability was excellent for all six cervical movements when compared with the research/clinical-grade CROM device, with intraclass correlation coefficients (ICCs) averaging 0.939. Bland–Altman plots confirmed close agreement between the two methods. Cervical movement trajectory curves further confirmed the concordance between the clinical device and our method. The system is fully automatic, requires only a rear-facing camera, effectively preserves patient privacy, and provides accurate cervical posture estimation. This technology may provide a basis for future applications in neck-disorder screening, remote health monitoring, and personalized musculoskeletal wellness management, although further task-specific clinical validation will be required. To date, HPNet has been validated primarily on a computer-based platform and has not yet been deployed on smartphones. Future work will focus on model lightweighting, mobile deployment, and cross-device adaptation to facilitate its practical implementation on mobile devices. Full article

Static pedobarometry is widely used to assess plantar pressure distribution and postural control; however, its clinical interpretation depends on measurement repeatability and reliability. The aim of our article is to evaluate the repeatability of plantar pressure parameters and the spatial localization of the center of pressure (CoP) and peak pressure (PP) during static bipedal standing. Five healthy adults underwent 30 repeated static measurements using the Sidas Press-Cam platform under standardized laboratory conditions. Analyzed parameters included contact area, mean pressure, peak pressure, and the center of pressure coordinates. Repeatability was assessed using intraclass correlation coefficients (ICC), standard error of measurement (SEM), and Bland–Altman analysis. Contact area showed high single-measure repeatability (ICC (2, 1) = 0.90). Peak pressure and mean pressure demonstrated moderate repeatability for single trials but excellent repeatability when averaged (ICC (2, k) = 0.97–0.996). The center of pressure exhibited greater mediolateral than anteroposterior variability. Peak pressure localization remained stable within 3 SD and predominantly on the dominant limb. Averaging multiple trials enhances measurement repeatability, suggesting that repeated static pedobarometric assessments may provide more consistent estimates of plantar pressure parameters. However, these findings should be interpreted as preliminary evidence from an intra-session repeatability study due to the limited sample size. Full article

In this paper, a controlled Picard semi-analytical technique is improved for a branch of nonlinear fractional stochastic delay differential equations since the nonlinear terms always prevent the establishment of closed-form solutions. The proposed approach extends the known Picard iteration by embedding a convergence-control parameter that improves the flexibility and stability of the iterative scheme while keeping the original mathematical formulation. A careful theoretical analysis is presented to establish the existence of the iterative sequence, its mean-square boundedness, convergence, and an explicit error estimate under standard Lipschitz continuity and linear growth assumptions. Moreover, a Numerical Picard implementation is updated to rebuild stochastic sample trajectories and to give an independent illustration through comparison with a predictor–corrector scheme. The presented methodology is applied to fractional stochastic models of human postural sway and logistic population models. The numerical results illustrate that the semi-analytical framework evaluates the expectation of and variance in the stochastic response for various fractional orders accurately. Although the semi-analytical controlled Picard method is incapable of performing a lot of iterations, it generates statistical moments that agree with those obtained using both the Numerical Picard and predictor–corrector methods, while explicit semi-analytical representations of the solution. These results show that the presented technique gives an accurate and effective approach for examining nonlinear fractional stochastic delay systems from biological, ecological, and engineering applications. Full article

The performance of physical workers is significantly influenced by the extent and quality of their motions. However, accurately measuring and assessing these motions remains a challenge due to the limitations in conventional instrumentation; wearable sensors require calibration and restrict mobility, while marker-based motion capture systems are costly and impractical for field deployment. Recent advancements have enabled in situ video analysis for the real-time observation of worker behaviors. To address these measurement constraints, this paper introduces a novel framework for tracking and quantifying upper and lower limb motions, issuing alerts when critical thresholds are reached. Using joint position data from posture estimation, the framework employs Hotelling’s $T^2$ statistic to quantify and monitor motion amounts. A significant positive correlation was noted between motion warnings and the overall NASA Task Load Index (TLX) workload rating (r = 0.218, p < 0.005). A supervised Random Forest model trained on the collected motion data was benchmarked across multiple datasets, including the in-house assembly dataset, G-AI-HMS, UCF Sports Action, UCF50, and PE-USGC. The proposed framework identified motion anomaly patterns with a maximum accuracy of 94% on the in-house assembly dataset, while performance varied across the external benchmark datasets. Full article

Non-contact body weight (BW) estimation provides a low-stress and low-cost approach for precision beef cattle management, but single-view RGB images may not fully capture body-shape information. This study proposed a practical dual-view RGB framework for cattle BW estimation. A total of 3210 paired top-view and side-view RGB images were collected from 107 Simmental beef cattle with BW ranging from 169 to 980 kg. An EMA-enhanced YOLO11n-seg model was adopted to improve cattle foreground extraction, and a two-stream CBAM-ResNet50-SE network was constructed to learn dorsal and lateral morphological features for BW regression. The EMA-YOLO11n-seg model achieved mAP@0.5 values of 99.18% and 98.35% for top-view and side-view images, respectively. On the test set, the proposed BW estimation model achieved an MAE of 14.96 kg, an RMSE of 17.86 kg, and an R² of 0.85. The model also showed stable performance across different growth stages and posture conditions. Adaptation experiments using a Sanhe cattle dataset further demonstrated the adaptability of the proposed framework. These results suggest that the practical dual-view RGB framework developed in this study provides an effective solution for non-contact beef cattle BW estimation under fixed image-acquisition conditions. Full article

Early layout decisions in in vitro diagnostic devices can affect operators’ viewing, reaching, and sample-loading actions. This study examined a concept-stage workflow that combined a low-fidelity bench with single-smartphone markerless motion capture before a complete engineering prototype was available. Interviews with seven engineers and task analysis were used to define posture-risk criteria, which were prioritized using the analytic hierarchy process (AHP). Twenty-five design students served as proxy operators. OpenCap-derived trajectories were processed in Python to calculate sagittal-plane joint angles and Rapid Upper Limb Assessment (RULA)-oriented screening indicators. In the primary near-sagittal subgroup (β = 0°, n = 14), the mean raw 99th percentile of neck flexion was 52.53° ± 7.55°, and all 14 participants exceeded the predefined 45° RULA-oriented threshold. The primary AHP–RULA analysis was restricted to this subgroup and provided a directional internal cross-check; the full-sample analysis was retained only as a sensitivity analysis. The workflow may support early layout screening and formative-evaluation documentation. Because no concurrent reference measurement system was used, the angle values should be interpreted as screening estimates rather than validated absolute measurements. The workflow does not replace usability validation conducted with intended clinical users and an engineering prototype. Full article

Bilateral vertical ground reaction force (vGRF) plates are the dominant sensor for field-deployed neuromuscular monitoring in athletes, with the countermovement jump (CMJ) being the dominant test. Detecting movement onset on the vGRF trace is a single decision that propagates through impulse–momentum integration to bias jump height. Existing onset method comparisons used fewer than 100 athletes, and bilateral weight rocking, a form of pre-jump postural sway during quiet standing, has not been characterized at scale as a method-specific source of disagreement. We analyzed 32,952 NCAA Division I CMJ trials from 579 athletes across 15 teams recorded on bilateral vGRF plates. Five hypotheses were pre-specified, and a per-trial decision rule was developed under leave-one-athlete-out cross-validation. Pooled, 45.89% of trials showed false-early firing on at least one of five non-first-derivative onset methods, relative to a rate-of-change reference, at a 30 ms threshold. On the rocking-amplified subset, a bidirectional body weight band method overestimated jump height by 0.943 cm versus the reference. The decision rule reached an area under the receiver operating characteristic curve of 0.71 with near-perfect calibration. It is a per-trial screening flag, not a deterministic classifier, and deployment should anchor on balanced-accuracy or high-precision operating points, holding the onset method constant across an athlete’s monitoring timeline. Full article

The transition from Industry 4.0 to Industry 5.0 is increasing the need for design approaches that place human centrality, safety, and ergonomics at the core of system development. In this context, immersive simulation is evolving from a training-oriented technology into a controlled environment for the observation of human behaviour and task execution. However, many Virtual Reality applications still rely on generic interaction devices that are not sufficiently representative of real tool-mediated operations, limiting the reliability of ergonomic and behavioural assessment. This paper proposes an anthropocentric framework for human-centred assessment in immersive simulation. The framework integrates four main components: a task-oriented Scenario Digital Twin, a Physical-Tool-in-the-Loop module based on a real instrument synchronised with its virtual counterpart, an Interaction Engine for state-dependent action management, and an Operator-in-the-Loop module coupled with a Human-Centred Assessment Layer. The framework is instantiated through an immersive hazelnut pruning simulator, selected as a representative case study because pruning involves non-neutral postures, irreversible actions, and strong dependence on tool handling. The reference implementation combines reality-based reconstruction of hazelnut trees, interactive branch-cutting logic, an instrumented electric pruning shear, full upper-body embodiment, and multimodal data acquisition. In particular, the proposed architecture supports the extraction of body motion through markerless multi-camera pose estimation and the acquisition of eye-related variables through the head-mounted display. A preliminary experimental session demonstrates the technical feasibility of the proposed architecture by showing that multimodal data, including reconstructed 3D body kinematics and eye-tracking signals, can be successfully acquired during immersive task execution, providing a basis for subsequent ergonomic analysis. The results show that a real instrumented tool, a task-oriented digital twin, and a continuous monitoring pipeline can be integrated within a single immersive platform, as well as that the assessment pipeline is sensitive to differing postural configurations during simulated task execution. They do not establish equivalence between behaviour in the simulator and behaviour during real-world pruning; dedicated cross-modal validation is therefore required. The framework is intended to support the evolution of immersive simulation toward assessment-oriented applications, contributing to a broader Safety-by-Design perspective in which human behaviour and interaction quality are considered as early-stage design inputs. Full article

Adaptive restraint systems require specific occupant information, including head position, anthropometry, and safety-relevant posture states. Existing 3D human pose estimation benchmarks mostly report root-relative pose, while automotive in-cabin studies rarely evaluate these outputs across heterogeneous vehicle sensor sets. We present ICF-Fusion, a five-modality transformer fusion architecture, and evaluate it under leave-one-subject-out (LOSO) validation on the ICF-Body dataset, which includes synchronized near-infrared (NIR) camera, 60 GHz millimeter-wave (mmWave) radar, belt webbing extraction sensor (WES), seat configuration sensor (SCS), and ultra-wideband (UWB) recordings. The model localizes the head with a Mean Root Position Error (MRPE) of 6.10 cm and regresses anthropometry to mean absolute errors (MAE) of 5.36 cm for height, 3.50 cm for torso length, 1.78 cm for shoulder width, and 8.61 kg for weight. Feet-on-dashboard is detected on 9 of 10 evaluable folds without meaningful MRPE degradation. The full sensor fusion outperformed every single modality on all three tasks, but NIR alone nearly matched it for head localization and feet-on-dashboard detection. The fusion advantage was substantial only for the anthropometry estimation task. Full article

Background: Prolonged smartphone use has been proposed to influence postural alignment and pulmonary function; however, continuous relationships among these variables remain uncertain in young adults. Methods: Thirty healthy university students completed assessments of device-recorded average daily smartphone screen time over the preceding seven days, craniovertebral and shoulder-C7 angles during natural standing, forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and positive and negative affect. Eight Spearman correlations were evaluated using 5000-resample bootstrap confidence intervals and Holm adjustment for multiple comparisons. Separate linear regression models examined smartphone screen time as a predictor of FVC and FEV1 after adjustment for biological sex and height. Results: None of the primary correlations was statistically significant after Holm adjustment. The largest estimate was observed between smartphone screen time and FVC (ρ = −0.242, 95% CI −0.540 to 0.101; Holm-adjusted p = 1.000). In adjusted models, smartphone screen time was not significantly associated with FVC (B = −0.054 L per additional hour/day, 95% CI −0.127 to 0.019; Holm-adjusted p = 0.280) or FEV1 (B = −0.027 L per additional hour/day, 95% CI −0.123 to 0.068; Holm-adjusted p = 0.561). Secondary exploratory analyses identified an inverse correlation between shoulder-C7 angle and negative affect and positive correlations between FVC and height and body weight. Conclusions: Statistically significant primary associations among seven-day smartphone screen time, standing postural alignment, and pulmonary function were not detected. These findings do not establish that the variables are independent and should be interpreted cautiously given the small sample and wide confidence intervals. Full article

Octopus arms combine a compliant continuum body with distributed suckers, providing a biological reference for soft manipulators that require large deformation and stable local contact. Inspired by this functional organization, this study presents an octopus-inspired two-segment pneumatic soft manipulator with passive suction cups at the distal end. The manipulator consists of a cylindrical proximal segment, a tapered distal segment, and a thermoplastic polyurethane (TPU) suction-cup array. The proximal segment provides structural support and global bending, whereas the tapered distal segment improves local compliance and contact posture adjustment near the target surface. Each segment contains three independently driven pneumatic chambers arranged at 120° intervals, enabling spatial bending through differential pressurization. The distal suction cups are not connected to an active vacuum source; instead, attachment is assisted by mechanical pressing, partial air expulsion from the cup cavity, and elastic recovery of the cup lip. Finite element simulations were conducted to examine pressure-driven bending of the soft arm and deformation of the suction cups under equivalent sealing loads. A piecewise constant curvature model was established to estimate the posture and reachable workspace of the two-segment manipulator. A prototype was fabricated and tested on a pneumatic control platform. Within the pressure range of 50–200 kPa, both segments exhibited increasing bending angles with increasing input pressure; at 200 kPa, the maximum observed bending angles were approximately 70° for the proximal segment and 87° for the distal segment. Distal-segment tests demonstrated passive contact holding on a brown glass bottle and a black roll of electrical tape. Coordinated actuation further produced compound bending and twisting postures. These results show that the proposed design translates the functional division of octopus arms into a modular pneumatic soft manipulator with controllable spatial deformation and passive distal contact support. Full article

To address systematic errors in prawn total length measurement caused by natural curvature and challenges in body mass prediction under random postures in aquaculture, this study proposes an automated phenotypic measurement and body mass prediction framework integrating skeleton-based nonlinear length estimation and multi-view feature analysis. Instance segmentation (YOLO11n-seg) obtains prawn head and abdomen masks. A skeleton-based procedure combining Zhang–Suen thinning, branch pruning, and graph-based path extraction obtains the projected body centerline for curved length measurement, while a curvature index quantifies body curvature. Body mass prediction models are built for side-view and top-view data, with SHAP analysis interpreting feature contributions. A unified random forest model using consistent morphological features enables body mass prediction across side-view and top-view samples. When evaluated against the independently acquired manual two-segment reference, the skeleton-based method achieved an MAE of 0.399 cm for severely curved prawns, representing reductions of 70.3%, 66.0%, and 21.9% relative to the straight-line method, MBR method, and Zhang–Suen baseline, respectively. On an independent test set, side-view and top-view models achieved mean absolute percentage errors of 5.73% and 5.82%, respectively. The unified RF model achieved an overall MAPE of 5.54%, and paired comparisons did not detect statistically significant differences from the corresponding viewpoint-specific models on either test subset. These results demonstrate the effectiveness of the proposed framework for non-contact prawn phenotyping under controlled imaging conditions. Full article

Background/Objectives: Diet quality, motor competence, and physical fitness are important domains of child development, yet their interrelationships during the preschool years remain insufficiently understood. This study examined whether diet quality was independently associated with motor competence and physical fitness and explored the potential role of motor competence and postural stability within these relationships in preschool children. Methods: A cross-sectional study was conducted in 134 preschool children aged 4–6 years. Motor competence was assessed using selected tasks from the Movement Assessment Battery for Children-2, and physical fitness was evaluated using tests from the PREFIT battery and the sit-and-reach test. Diet quality was estimated using a food frequency questionnaire from which a Diet Quality Index (DQI) was derived. Multiple linear regression models adjusted for age, sex, and body mass index were used to examine the associations among study variables. Exploratory pathway analyses and prespecified sensitivity analyses were additionally performed. Results: Diet quality showed a positive but non-significant association with motor competence, whereas no significant direct association with physical fitness was observed after adjustment for age, sex, and body mass index. Exploratory pathway analyses likewise did not provide evidence of statistically significant indirect associations between diet quality and physical fitness through motor competence, although the direction of the observed associations was consistent across sensitivity analyses. In addition, higher Global Motor Competence and Postural Stability were consistently associated with better global physical fitness, lower-limb muscular strength, cardiorespiratory fitness, flexibility, and speed/agility. Conclusions: The present study found no evidence of significant direct or indirect associations between diet quality and physical fitness after multivariable adjustment, although healthier dietary patterns showed consistently favorable trends. Consistent with previous evidence, motor competence, particularly postural stability, was positively associated with multiple components of physical fitness. Full article

High renewable penetration makes day-ahead operation sensitive to the directional effects of wind and photovoltaic forecast errors. Conventional prediction intervals mainly evaluate coverage and sharpness, but lower- and upper-boundary violations correspond to different operational risks: shortage-side supply-adequacy pressure and accommodation-side curtailment pressure. This paper proposes context-aware asymmetric conformal quantile regression (CA-ACQR) to construct directional renewable-power risk intervals. The method builds separate conformal scores for the two interval sides, estimates context-dependent boundary corrections, and reallocates the tail-risk budget under supply-priority, balanced, and accommodation-priority profiles. Case studies use regional wind and photovoltaic power data, with contextual groups defined by renewable type, lead-time block, forecast difficulty, weather-risk regime, and output level. CA-ACQR increases the prediction interval coverage probability (PICP) from 91.11% to 94.07% and reduces the accommodation-side violation rate from 4.37% to 1.44%. The results demonstrate selectable directional risk postures and quantify trade-offs among interval width, directional violations, normalized stress cost, and the 95% conditional value-at-risk stress cost. Full article

Background/Objectives: Postural control depends on the integration and reweighting of visual, somatosensory, vestibular, and contextual sensory information. Stable auditory cues may support balance, whereas complex musical stimulation may impose additional sensory-cognitive demand during multisensory conflict. This study examined the acute effects of relative device-output music intensity on virtual-reality-based postural control in trained athletes and explored whether responses differed by sport background. Methods: Forty-eight athletes from tennis, combat sports, swimming, football, and volleyball completed the Clinical Test of Sensory Interaction in Balance delivered through virtual reality (CTSIB-VR) and Limits of Stability (LOS) assessments under four auditory conditions: routine/no sound and low (+10 dB), moderate (+20 dB), and high (+30 dB) relative device-output increments. Linear mixed-effects models included sport, auditory condition, and their interaction as fixed effects and participant-specific random intercepts and random linear condition slopes. Model-based estimated marginal means, Bonferroni-adjusted contrasts, 1.5×IQR sensitivity analyses, and robust generalized estimating equations were calculated. Results: Auditory condition affected all five CTSIB-VR outcomes (Wald χ²(3) = 16.773–94.404, all p < 0.001). The routine condition exceeded the high-intensity condition for composite score (adjusted mean difference = 6.05, 95% CI 3.99–8.10; Bonferroni-adjusted p < 0.001) and somatosensory score (8.62, 95% CI 6.78–10.46; adjusted p < 0.001). Sport × condition interactions were significant for all CTSIB-VR outcomes (χ²(12) = 54.869–98.953, all p < 0.001), but sport-stratified findings were exploratory. For LOS, auditory-condition effects were detected for endpoint excursion (p = 0.004), maximum excursion (p < 0.001), and directional control (p = 0.002), whereas reaction time (p = 0.648) and movement velocity (p = 0.056) did not show clear main effects. Sensitivity analyses supported the endpoint-excursion, maximum-excursion, and directional-control findings; movement-velocity inference was method-sensitive. Conclusions: Relative device-output music intensity was associated with consistent changes in CTSIB-VR sensory-organization measures and outcome-specific changes in LOS performance. Sport-related patterns require confirmation in adequately powered, balanced samples. Full article