Search Results (3,396)

Multimodal human–AI systems generally consider facial expressions and body motions as separate input streams, leading to disjointed interpretations and diminished emotional coherence. To overcome this issue, we offer the Engagement-Safe Expressive Alignment (ESEA) paradigm and the Unified Visual Synchrony (UVS) framework as its computational implementation. UVS models the coherence between facial expressions and gestures, offering an interpretable visual synchrony signal that can function as adaptive feedback in human–AI interactions. The framework’s key component is the Consistency Index for Affective Synchrony ($CIAS$), which correlates brief visual segments with scalar synchrony scores through a common latent representation. Facial and gestural signals are processed by modality-specific projection networks into a unified latent space, and $CIAS$ is derived from the similarity and short-term temporal consistency of these latent trajectories. The synchrony index is regarded as an estimation of affective visual coherence within the ESEA paradigm. We formalize the UVS/$CIAS$ framework and conduct a comparative experimental evaluation utilizing matched and mismatched face–gesture segments derived from rendered dialog footage. Utilizing ROC analysis, score distribution comparisons, temporal visualizations, and negative control tests, we illustrate that $CIAS$ effectively captures structured face–gesture alignment that surpasses similarity-based baselines, while also delivering a persistent, time-resolved synchronization signal. These findings establish $CIAS$ as a principled and interpretable feedback signal for future affect-aware, engagement-focused multimodal agents. Full article

As the maritime industry transitions towards green shipping, operational sustainability and energy efficiency are increasingly crucial for long-endurance Unmanned Surface Vehicle (USV) missions. To this end, proactively adjusting driving strategies based on the prediction of other USVs’ motion is essential. This proactive approach directly minimizes carbon emissions and reduces high-energy driving behaviors resulting from passive sudden braking or sharp turns in unexpected situations. However, existing trajectory prediction methods are trained based on low-frequency automatic identification system data of large merchant vessels, which cannot be directly used on the highly dynamic USV data. To address this limitation, this study constructs a large-scale simulated USV scenario dataset grounded in nonlinear ship hydrodynamics, which contains complicated interactive scenarios with multiple USV agents. To effectively model the interaction among agents for accurate prediction, we further propose USV-Former, a hierarchical encoder-decoder architecture designed for proactive navigation. The framework integrates a symmetric encoding structure with a dual-stage pipeline: a Local Attention Module captures high-frequency dynamics, while a Global Graph Attention Module enforces COLREGs-compliant topological constraints. Experimental results demonstrate that the proposed model outperforms established baselines in prediction accuracy. Qualitative analysis further reveals that by accurately anticipating target intentions, the model minimizes unnecessary avoidance maneuvers, enabling more stable and momentum-conserving velocity profiles. Ultimately, this architecture exhibits high computational efficiency, reduces operational energy waste, and provides a robust, measurable algorithmic foundation for green autonomous shipping and marine environmental protection. Full article

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems. Full article

This study examines the hydrodynamic performance and energy conversion mechanisms of a dual-float wave energy converter (WEC) to address the limitations of single-float WECs regarding energy capture efficiency and cost-effectiveness. A three-dimensional numerical wave tank is constructed utilizing computational fluid dynamics (CFDs) technology and STAR-CCM+ to simulate the dynamic response of the dual-float system under specific wave conditions characterized by a height of 0.1 m and a period of 1.5 s. The effects of a front-rear configuration with a quarter-wavelength spacing on the converter’s power output, turbofan rotational characteristics, and heave motion are systematically analyzed. The results indicate that the wave-facing float attains a consistent rotational speed of 4 rad/s, exhibiting significant fluctuations in heave displacement and velocity. Conversely, the downstream float exhibits diminished motion amplitude, a constant rotational velocity of 2.5 rad/s, and curtailed power generation attributable to wave diffraction and energy shielding from the wave-facing float. The mutual hydrodynamic interference between the floats influences the total energy conversion efficiency, as evidenced by the dual-float system’s array impact factor of 0.989. A parametric study covering multiple wave conditions and float spacing is supplemented to reveal the influence law of key parameters on system performance. This paper elucidates the fundamental mechanism of hydrodynamic coupling in dual-float arrays and offers a theoretical foundation and technical guidance for the optimal design and engineering application of arrayed WECs. Full article

Three-dimensional (3D) glacier velocities capture the full dynamic behavior of ice masses. For marine-terminating glaciers, acquiring 3D velocity fields is particularly critical for quantifying ice discharge into the ocean, assessing the stability of floating ice tongues, and constraining ice–ocean interactions that govern submarine melting, calving processes, and freshwater fluxes to the ocean. To further investigate glacier dynamics and elucidate ice–ocean interaction mechanisms, this study analyzed the 3D velocity of the Petermann Glacier throughout 2021 using long-term Sentinel-1 synthetic aperture radar (SAR) observations. First, two-dimensional velocity time series were derived from ascending and descending SAR images, and the glacier’s 3D velocity components were reconstructed based on the geometric relationships between the two viewing geometries. The estimated 3D velocities were then used as prior constraints, and glacier motion was treated as a continuously evolving state variable within a Kalman filtering framework. Multi-track, asynchronous remote sensing observations were integrated into a unified system to obtain a stable and temporally continuous 3D velocity field. Finally, statistical analyses of the 3D velocity time series were conducted to characterize spatiotemporal variations, seasonal patterns, and topographic influences on glacier motion, thereby providing quantitative insights into the dynamic coupling between glacier and ocean. Full article

Background: Virtual, augmented, and mixed reality (or collectively extended reality, XR) serious games, combined with motion-tracking technologies, are increasingly used for motor and cognitive rehabilitation and training. As XR and tracking technologies advance, a systematic mapping of the related research area could offer relevant insights. Objectives: This review aims to map interactive XR serious games, using motion-tracking technologies for physical or cognitive rehabilitation or training, and describe intervention characteristics and evaluation methods. Eligibility Criteria: Eligible studies were English, peer-reviewed journal articles published between 2015 and October 2025, with more than three participants, using custom XR serious games for rehabilitation or training. Studies were excluded if they focused on technical aspects, passive XR, diagnostic evaluation, psychological therapies, minor participants, procedural training, or education. Charting Methods: Data were charted using a structured form capturing XR characteristics, hardware configurations, study characteristics, and evaluation methods. Results: 61 studies were included. Most employed non-immersive or fully immersive VR interventions, targeting physical upper-body rehabilitation, especially post-stroke and Parkinson’s disease. Usability, acceptability and user experience, and training effectiveness were commonly evaluated with positive outcomes. Conclusions: The findings highlight opportunities for research into augmented and mixed reality approaches, particularly for cognitive function, and use of XR-based interventions across broader populations. Full article

Continuous blood pressure (BP) monitoring is essential for the prevention and management of cardiovascular diseases. Traditional cuff-based methods cause discomfort during repeated measurements, and wearable sensors require direct skin contact, limiting their applicability. Radar-based contactless BP measurement has emerged as a promising alternative. However, radar pulse wave (RPW) signals are susceptible to motion artifacts, respiratory interference, and environmental clutter, posing persistent challenges to estimation accuracy and robustness. In this paper, we propose MARU-MTL, a Mamba-enhanced multi-task learning framework for continuous BP estimation using a single millimeter-wave radar sensor. To address signal quality degradation, a Variational Autoencoder-based Signal Quality Index (VAE-SQI) mechanism is proposed to automatically screen RPW segments without manual annotation. To capture long-range temporal dependencies across cardiac cycles, we integrate a Bidirectional Mamba module into the bottleneck of a U-Net backbone, enabling linear-time sequence modeling with respect to the segment length. We also introduce a multi-task learning strategy that couples BP regression with arterial blood pressure waveform reconstruction to strengthen physiological consistency. Extensive experiments on two datasets comprising 55 subjects demonstrate that MARU-MTL achieves mean absolute errors of 3.87 mmHg and 2.93 mmHg for systolic and diastolic BP, respectively, meeting commonly used AAMI error thresholds and achieving metrics comparable to BHS Grade A. Full article

3D electrodeposition printing is an emerging process for fabricating metallic parts with controllable geometry, yet the coupled influences of electrochemical kinetics, ion transport, and tool motion on layer height remain difficult to interpret. This work presents a physics-based process model that links key process inputs, current density, electrolyte concentration, the inter-electrode gap, and tool scanning speed, to the resulting layer height in 3D electrodeposition printing of nickel-based structures. The model combines species transport in the inter-electrode gap with Butler–Volmer kinetics, under carefully stated assumptions regarding current efficiency, overpotential, and lateral spreading. Model predictions are validated against experimentally reported layer heights over a range of process conditions, yielding average errors (9–15%) and root-mean-square errors (0.13–0.28 µm) that demonstrate good agreement and highlight the impact of simplifying assumptions. Systematic parametric studies reveal how each process input monotonically influences layer height in ways consistent with Faraday’s law and diffusion-controlled growth, while also quantifying the relative sensitivity to different parameters. Building on these results, we introduce a dimensionless 3D Electrodeposition Printing Index that consolidates the key process and material parameters into a single scalar describing the geometric growth regime. The index enables construction of process maps that capture how combinations of current density, scan speed, concentration, and gap affect achievable layer height within the validated operating window. The scope and limitations of the proposed modeling framework and the index, particularly regarding other materials, more complex geometries, and pulsed or strongly convective regimes, are explicitly discussed, providing a basis for future model extensions and experimental validation. Full article

Liquefied natural gas (LNG) sloshing occurs during marine transportation and storage due to vessel motion or external disturbances, leading to complex fluid–structure interactions within the containment system. This study employs OpenFOAM to develop a numerical model of LNG sloshing. The model solves the incompressible multiphase Navier–Stokes equations and utilizes the Volume of Fluid (VOF) method to capture the dynamic behavior of gas–liquid interface. The numerical model was validated against experimental data. Based on this model, the key hydrodynamic characteristics are investigated for LNG sloshing, including nonlinear free surface, transient pressure distribution on the tank walls due to liquid impact, and energy dissipation mechanisms. By varying excitation frequencies, amplitudes, and the configuration of internal components such as baffles or anti-sloshing devices, the study explores the sloshing response and effective control strategies. The results indicate that appropriately designed baffles can significantly mitigate sloshing-induced impact pressures on tank walls and enhance system stability. In the future, this study could extend to multi-layer fluids, multi-degree-of-freedom motions, and simulations under more complex real-world conditions. Full article

Gibson’s concept of optic flow established that perception is grounded in lawful structure generated by action. However, no formal mechanical framework has described the invariant structure of action-generated kinesthetic information during skilled manipulation. This study introduces haptic flow as a screw-theoretic invariant defined by the coupled rotational–translational organization of a body–object system. Motion capture data from a two-case comparison (one proficient and one novice golfer) were analyzed using instantaneous screw axes (ISA), pitch evolution, and cylindroid geometry derived from a linear line-complex formulation. The proficient golfer exhibited (1) progressive convergence of ISAs toward a coherent bundle, (2) stabilization of screw pitch through impact, and (3) co-cylindrical alignment of harmonic screws consistent with inertial–restoring conjugacy. In contrast, the novice golfer showed fragmented ISA organization and elevated pitch variability. These differences were descriptive rather than inferential and do not imply population-level generalization. The findings suggest that skilled manipulation is characterized by stabilization of symmetry-bearing screw invariants rather than by independent joint control. Interpreted ecologically, haptic flow is proposed as a mechanically specified candidate invariant generated by lawful body–object coupling. The present study establishes a geometric framework for quantifying such invariants while identifying the need for cross-task and perceptual validation. Full article

The location of the rider centre of mass (CoM) is especially relevant in bicycles and motorcycles due to the large human-to-vehicle mass ratio. This work illustrates two alternative methods for the experimental identification of the longitudinal and lateral coordinates of the rider CoM position as a function of the posture. The first method uses a set of load cells and provides accurate and reliable results. However, riders’ must firmly hold their configuration for the time necessary to stabilise the force measurements, which may be uncomfortable in configurations such as lean-out. The second method utilises an optical system which captures the rider attitude. This information is then used to feed a multibody model, which is used to estimate the CoM coordinates. Full article

Autonomous vehicles (AVs) must make predictable and socially compliant behavioral decisions to ensure safe and efficient interactions with other road users. To address this challenge, this paper proposes a game-theoretic behavioral decision-making model integrated with spatial motion planning to capture the interactive intentions between the ego vehicle (EV) and target vehicle (TV) in pairwise scenarios. First, the study defines an intention representation method that characterizes intentions using spatial area boundaries, feasible speed ranges, and a set of goal points (speed goal points, position-orientation goal points). Second, a spatial motion planning approach is adopted to evaluate the intention, which optimizes the driving scheme using a multi-objective cost function (incorporating pursuit precision, comfort, energy efficiency, and travel efficiency). Finally, the game-theoretic decision-making model is constructed. The Social Value Orientation (SVO) is introduced to quantify drivers’ social preferences, and the payoff function, which integrates safety rewards (based on inter-vehicle distance) and performance rewards (based on motion planning indices), is established. Simulation results verify that the proposed model can effectively address the interactive intention decision-making problem between the AV and other road users and handle different scenarios. Full article

Tire-Derived Aggregate (TDA) is a recycled fill material made by cutting scrap tires into small pieces that satisfy the gradation requirements in ASTM D 6270. Since its introduction to civil engineering applications, TDA fill and TDA backfill have been successfully implemented in many projects. However, the dynamic behavior of the TDA backfill under significant earthquakes has not been substantially addressed. The present study used nonlinear time-history Finite Element Analysis (FEA) to analyze the dynamic behavior of a retaining wall with TDA backfill captured from the full-scale shake table test. Unlike typical soil failure observed in a similar retaining wall with conventional soil backfill, significant wall sliding occurred because lightweight TDA contributed to reducing the friction resistance of the wall footing. Therefore, the analysis required modeling capability of rigid body motion and impact loading from the separation between the wall stem and the backfill. With adequate friction models and softened contact models, the FEA generated the dynamic motion of the retaining wall that matched well with the measured responses, including the wall sliding. The friction model between the wall footing and soil was most critical in accurately reproducing wall sliding motion. It was determined to use different friction coefficients for the two different earthquakes used in the study in order to simplify the rate dependence of the coefficient. Also, the softened contact model generated more reasonable impact force by allowing overclosure and finite stiffness during impact. The FEA model and modeling technique in the present study can be used for the seismic design of various field-scale retaining walls with TDA backfill. Full article

Home based telerehabilitation has expanded after COVID-19, but delivering timely guidance and monitoring exercise performance outside the clinic remains difficult. Traditional physiotherapy often relies on repeated execution of simple routines, yet clinicians have limited visibility into adherence and movement quality during unsupervised sessions. From a systems perspective, many telerehabilitation approaches also face constraints in accessibility, bandwidth, and computational cost that can limit practical deployment. This paper presents a modular telerehabilitation framework and prototype that captures and records rehabilitation exercise sessions for asynchronous clinician review in a 3D visualisation environment. The system integrates skeletal motion capture with plantar pressure sensing, and stores sessions as portable artefacts to support replay, inspection, and downstream analysis. A connector-based architecture enables extension to additional sensors without redesigning the core application, and the design aims to support deployment under constrained home computing and networking conditions. The manuscript contributes an implementation blueprint and reference architecture for multimodal capture and replay. Clinical effectiveness, usability outcomes, and quantitative sensor accuracy benchmarking are outside the scope of this work and are identified as necessary future evaluation. Full article

Markerless motion capture (MMC) provides a non-invasive alternative for motion analysis; however, its validity at the standard frame rate of 25 Hz commonly used in broadcast and surveillance applications remains to be established. This study evaluated the performance of a 25 Hz multi-camera MMC workflow using consumer-grade cameras for capturing basketball-specific movements. Three highly trained male athletes completed seven tasks, including sprinting and simulated sport-specific skills, while being synchronously recorded by six MMC cameras (DJI Action 5 Pro, 25 fps) and a 10-camera Vicon system (25 Hz). Kinematic data were processed using an RTMDet–RTMPose pipeline and low-pass filtered at 6 Hz. Waveform validity was assessed using Pearson’s correlation coefficient (r) and the root mean square error (RMSE). The displacement magnitudes of 12 joints showed excellent agreement (r = 0.916–0.994; median nRMSE = 0.54–1.32%), indicating robust trajectory reconstruction. In contrast, agreement decreased for derivative variables: velocity (r = 0.583–0.867) and acceleration (r = 0.232–0.677) were highly sensitive to the low sampling rate and numerical differentiation. Although a 25 Hz configuration is insufficient for high-precision impact analysis, it provides acceptable accuracy for macroscopic displacement tracking and external-load quantification in resource-constrained training environments. Future optimization should prioritize temporal synchronization to improve the reliability of derivative variables. Full article