Search Results (147)

This study develops a reduced-order predictive model of the Sit-To-Stand (STS) task to examine whether a simplified biomechanical representation can reproduce key STS patterns reported in the literature and to investigate the role played in movement by a flexible trunk. The model represents the human body as a planar multibody system and formulates STS as an optimization problem within a discrete mechanics framework. This formulation combines reduced model complexity, explicit torso flexibility, and a structure-preserving numerical approach for trajectory generation. Simulations were used to evaluate the effects of movement duration, reduced joint strength, and seat height on joint torques, kinematics, trunk motion, and ground reaction forces (GRFs). The results reproduced several qualitative trends reported in previous experimental studies, including increased peak joint torques and GRFs with shorter movement duration, lower joint strength, and reduced seat height, as well as greater compensatory trunk motion under more demanding conditions. These findings suggest that the proposed framework captures key adaptive features of STS mechanics and may provide useful insights for rehabilitation analysis and the design of assistive technologies such as lower-limb exoskeletons and rehabilitation devices. At the same time, the present work should be regarded as an initial methodological study, since validation is currently qualitative and further experimental calibration, quantitative validation, and sensitivity analysis remain part of ongoing work. Full article

This study aims to determine the current seismic resistance of two masonry minarets that were severely damaged during the 6 February 2023 Kahramanmaraş earthquakes, while also evaluating whether a model-updating approach based on experimental dynamic characteristics can reliably capture the actual seismic behavior and collapse mechanism of such structures under real earthquake conditions. The dynamic characteristics of the minarets were identified using Operational Modal Analysis (OMA) based on previous in-situ vibration measurements. These characteristics were used to calibrate finite element models through a model-updating process employing Multi-Criteria Decision-Making (MCDM) methods. The initial modal analyses revealed discrepancies of up to 13.7% in natural frequencies and 9.7% in mode shapes. After applying MCDM methods to a wide set of model variants, these differences were reduced to 2.0% and 9.2%, respectively, improving the agreement between numerical and experimental results. Once the most representative models were obtained, nonlinear seismic analyses were performed using actual ground motion records from the earthquake. The results included evaluations of peak displacements, base shear forces, and principal stresses. The concentration of principal stresses near the transition zone showed good qualitative agreement with the observed collapse locations, indicating a reasonable consistency between numerical results and observed damage patterns. These findings demonstrate the value of integrating OMA-based model updating with MCDM methods and support a data-driven framework for assessing the seismic performance of historical masonry structures. Full article

Widespread acceptance of collaborative robots in human-involved scenarios requires accessible and intuitive interfaces for lay workers and non-expert users. Existing interfaces often rely on users to plan and issue low-level commands, necessitating extensive knowledge of robot control. This study proposes a multimodal agentic AI framework integrating natural user interfaces (NUIs) to foster effortless human-like partnerships in human–robot collaboration (HRC), which enhance intuitiveness and operational efficiency. First, it allows users to instruct robots using plain language verbally, coupled with gaze, revealing objects precisely. Second, it offloads users’ workload for robot motion planning by understanding context and reasoning task decomposition. Third, coordinating with AI agents built on large language models (LLMs), the system interprets users’ requests effectively and provides feedback to establish transparent communication. This proof-of-concept study included experiments to demonstrate a practical implementation of the agentic AI framework on a mobile manipulation robot in the collaborative task of human–robot wood assembly. Seven participants were recruited to interact with this AI-integrated agentic robotic system. Task performance and user experience metrics were measured in terms of completion time, intervention rate, NASA TLX survey for workload, and valuable insights of practical applications were summarized through a qualitative analysis. This study highlights the potential of NUIs and agentic AI-embodied robots to overcome existing HRC barriers and contributes to improving HRC intuitiveness and efficiency. Full article

Background: Qualitative motion analysis revealed that the cervical spine moves according to a consistent pattern. Current data analysis methods are limited by the extensive time required to process the retrieved data. A previous study demonstrated the feasibility of using a deep-learning model to automate analysis methods. However, segmentation accuracy needed to be improved. This study aims to improve segmentation model performance to enable reliable motion analysis. Methods: Four nnU-Net configurations were tested: baseline (A), pre-trained (B), with histogram equalization (C), and pre-trained with histogram equalization (D). Segmentation performance was evaluated using Dice Similarity Coefficient (DSC), Intersection over Union (IoU) and 95th percentile Hausdorff Distance (HD95). Vertebral rotation was estimated using mean shapes. Reliability was assessed using the Intraclass Correlation Coefficient (ICC). Sensitivity analyses were conducted. Results: Across all models, mean DSC ranged from 0.67 to 0.92, mean IoU from 0.55 to 0.85, and mean HD95 from 2.35 to 19.67 mm. After sensitivity analysis for low segmental range of motion (sROM) and low-quality recordings, the mean ICC ranged from 0.617 to 0.837 for model A, from 0.609 to 0.780 for model B, from 0.409 to 0.689 for model C, and from 0.480 to 0.835 for model D. Conclusions: This study shows that Models A and B can accurately analyze cervical motion patterns. High image contrast and an adequate sROM are essential for robust model performance. It also marks an important step toward automated qualitative motion analysis, increasing the accessibility of motion pattern evaluation. 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

Breaststroke performance is highly dependent on lower-limb biomechanics and the coordination of movement during the kick cycle. Recent advances in wearable inertial sensor technology enable objective analysis of human motion in real training environments. This study presents an exploratory pilot investigation aimed at evaluating the feasibility of using wearable inertial sensors for biomechanical analysis of the breaststroke kick in young swimmers. Five male children (aged 8–10 years) with basic breaststroke proficiency participated in a single-group pre–post exploratory study conducted over a three-month period. Lower-limb motion was monitored using wearable inertial measurement units attached bilaterally to the shanks and feet, allowing real-time kinematic feedback and data recording during training sessions. The intervention consisted of five structured training sessions integrating drill-based breaststroke kick exercises with sensor-assisted feedback. Outcome measures included time-based swimming performance tests (40 m breaststroke kick with kickboard and 40 m breaststroke without kickboard) and qualitative biomechanical evaluations of the passive and active phases of the breaststroke kick. Additionally, selected IMU-derived kinematic variables (peak ankle dorsiflexion and external foot rotation angles) were analyzed to provide quantitative biomechanical insight. Following the intervention, improvements were observed across all outcome measures, including reduced swimming times and increased technique scores assigned by two independent evaluators. These findings support the feasibility of integrating wearable IMUs for technique monitoring and simple kinematic quantification of breaststroke kick mechanics in young swimmers; larger controlled studies are required to assess efficacy. Full article

Background: Alpine skiing imposes high biomechanical demands on the lower limbs, which are further amplified in individuals with transfemoral amputation due to prosthetic constraints. This study aimed to quantify three-dimensional knee flexion asymmetries during alpine skiing turns in transfemoral amputee skiers compared with non-disabled controls. Methods: Five unilateral transfemoral amputee skiers (intervention group) and five non-disabled ski instructors (control group) performed six left and six right turns on a skiing simulator under laboratory conditions. Knee flexion angles at the apex of each turn were analyzed using three-dimensional motion capture. Intra-individual differences between the prosthetic and intact limbs were assessed using paired comparisons, and inter-individual differences between groups were evaluated using independent statistical tests (p < 0.05), performed in IBM SPSS Statistics. Results: Intra-individual analysis revealed significant knee flexion asymmetries (p < 0.05) in almost all amputee participants at the apex of both left (mean difference = 7.74°, 95% CI: 3.38–12.09) and right turns (mean difference = 4.36°, 95% CI: 2.66–6.06). In the control group, asymmetries were smaller and reached significance only for the inside leg in both turns (mean difference = 4.02°, 95% CI: 2.51–5.54). Inter-individual comparisons demonstrated significant differences between the groups for both turning directions. During left turns (prosthetic limb on the inside), the largest difference was observed for the inside leg (26.9°, p < 0.001), while the smallest difference occurred for the outside leg (12.1°, p = 0.013). During right turns (prosthetic limb on the outside), the largest difference was found for the outside leg (19.0°, p < 0.001), with a smaller but still significant difference for the inside leg (14.0°, p < 0.001). Conclusions: Transfemoral amputee skiers exhibit a turning strategy that is qualitatively comparable to that of non-disabled skiers; however, it is characterized by a reduced knee flexion range of motion. These limitations appear to be primarily influenced by prosthesis mechanics and user-specific skill levels rather than by a fundamentally different movement strategy. Full article

In recent years, many countries have increased their investment in the field of humanoid robots, promoting significant technological development. This study aims to enable humanoid robots to better adapt to various complex environments, enhancing the robustness of their motion systems and the generalization ability of their motion strategies. Using reinforcement learning algorithms, training on varied terrain is a critical factor for developing adaptable humanoid robots. This paper takes the humanoid robot G1 as the research platform. First, it completes the training, transfer verification, and real-machine deployment of a flat-ground walking model. Then, using fuzzy logic control and a phased training strategy, walking models for ascending/descending stairs and traversing slopes are trained. By systematically varying the stair height and slope gradient, the convergence of the reward function and the task completion success rate are analyzed. Furthermore, the dynamic stability of the robot on complex terrains is validated through qualitative kinematic analysis. The research concludes that as the single-step height and slope gradient increase, the reward value initially rises with more iterations but converges more slowly and at a lower final value. Statistical analysis shows that the success rates of phased training for stair and slope terrains are higher than 86% and 92%, respectively. Full article

Teacher preparation is an emotional as well as a cognitive process in which pre-service teachers must develop both reflective judgment and the emotional resilience needed for demanding instructional contexts. This study examined how university-based supervisors enacted the relational spaces of the University-Based Coaching Framework (UBCF) and how these enactments shaped pre-service teachers’ emotional and reflective development. Drawing on qualitative analysis of coaching discourse among three supervisor-pre-service teacher pairs, the comparative case study identifies distinct coaching identities that emerged from supervisors’ patterned relational moves. These identities corresponded to varying intensities of UBCF space enactment and produced differential pathways through a reflective-motional cycle connecting appraisal, coping, and reappraisal. Findings demonstrate that supervisors’ relational stance functions as both cognitive scaffolding and as an emotional regulator. By conceptualizing UBCF-based coaching as an interactional process that integrates relational attunement with reflective challenge, this study contributes new insight into how emotional and cognitive dimensions of supervision jointly support teacher knowledge development and early professional resilience. Full article

Accurate estimation of tree biomass and volume is essential for sustainable forest management, climate change mitigation, and ecosystem service assessment. Recent advances in unmanned aerial vehicle (UAV) technology enable the acquisition of ultra-high-resolution optical and three-dimensional data, providing a resource-efficient alternative to traditional field-based inventories. This review synthesizes 181 peer-reviewed studies on UAV-based estimation of tree biomass and volume across forestry, agricultural, and urban ecosystems, integrating bibliometric analysis with qualitative literature review. The results reveal a clear methodological shift from early structure-from-motion photogrammetry toward integrated frameworks combining three-dimensional canopy metrics, multispectral or LiDAR data, and machine learning or deep learning models. Across applications, tree height, crown geometry, and canopy volume consistently emerge as the most robust predictors of biomass and volume, enabling accurate individual-tree and plot-level estimates while substantially reducing field effort and ecological disturbance. UAV-based approaches demonstrate particularly strong performance in orchards, plantation forests, and urban environments, and increasing applicability in complex systems such as mangroves and mixed forests. Despite significant progress, key challenges remain, including limited methodological standardization, insufficient uncertainty quantification, scaling constraints beyond local extents, and the underrepresentation of biodiversity-rich and structurally complex ecosystems. Addressing these gaps is critical for the operational integration of UAV-derived biomass and volume estimates into sustainable land management, carbon accounting, and climate-resilient monitoring frameworks. Full article

This work explores the qualitative dynamics of the modified unstable time-fractional nonlinear Schrödinger equation (mUNLSE), a model applicable to nonlinear wave propagation in plasma and optical fiber media. By transforming the governing equation into a planar conservative Hamiltonian system, a detailed bifurcation study is carried out, and the associated equilibrium points are classified using Lagrange’s theorem and phase-plane analysis. A family of exact wave solutions is then constructed in terms of both trigonometric and Jacobi elliptic functions, with solitary, kink/anti-kink, periodic, and super-periodic profiles emerging under suitable parameter regimes and linked directly to the type of the phase plane orbits. The validity of the solutions is discussed through the degeneracy property which is equivalent to the transmission between the phase orbits. The influence of the fractional derivative order on amplitude, localization, and dispersion is illustrated through graphical simulations, exploring the memory impacts in the wave evolution. In addition, an externally periodic force is allowed to act on the mUNLSE model, which is reduced to a perturbed non-autonomous dynamical system. The response to periodic driving is examined, showing transitions from periodic motion to quasi-periodic and chaotic regimes, which are further confirmed by Lyapunov exponent calculations. These findings deepen the theoretical understanding of fractional Schrödinger-type models and offer new insight into complex nonlinear wave phenomena in plasma physics and optical fiber systems. Full article

Background: General Movement Assessment is a strong early predictor of adverse neurodevelopmental outcomes but remains qualitative and examiner-dependent. Quantitative, video-based kinematic analysis may complement General Movement Assessment by providing objective, scalable metrics. Methods: In this pilot study, a computer–vision-based pipeline was used to extract trunk center-of-mass kinematics from video recordings of spontaneous General Movements in infants under three months corrected age during the Writhing and Fidgety stage. Two measures were derived: trunk quantity of motion and movement duration. Group differences were examined using t-tests and effect sizes, and associations with corrected age and sex were explored with correlation analyses. Results: Writhing Movements were substantially longer than Fidgety Movements, with a large effect size, whereas trunk quantity of motion did not differ meaningfully between movement types. Correlations between corrected age and both the quantity of motion and duration were small and imprecise. Sex did not moderate duration changes, but trunk motion showed a significant age–sex interaction effect. Conclusions: Video-based extraction of trunk kinematics is feasible in early infancy and reveals robust differences in GMs type duration between Writhing and Fidgety Movements. Larger longitudinal studies are needed to clarify the value of these measures as early quantitative markers of postural control and neuromotor development. Full article

This study outlines the design and evaluation of a biomimetic robotic hand tailored for Human–Robot Interaction (HRI), focusing on improvements in tactile fidelity driven by material choice. Thermoplastic polyurethane (TPU) was selected over polylactic acid (PLA) based on its reported elastomeric characteristics and mechanical compliance described in prior literature. Rather than directly matching human skin properties, TPU was perceived as providing a softer and more comfortable tactile interaction compared to rigid PLA. The robotic hand was anatomically reconstructed from an open-source model and integrated with AX-12A and MG90S actuators to simplify wiring and enhance motion precision. A custom PCB, built around an ATmega2560 microcontroller, enables real-time communication with ROS-based upper-level control systems. Angular displacement analysis of repeated gesture motions confirmed the high repeatability and consistency of the system. A repeated-measures user study involving 47 participants was conducted to compare the PLA- and TPU-based prototypes during interactive tasks such as handshakes and gesture commands. The TPU hand received significantly higher ratings in tactile realism, grip satisfaction, and perceived responsiveness (p < 0.05). Qualitative feedback further supported its superior emotional acceptance and comfort. These findings indicate that incorporating TPU in robotic hand design not only enhances mechanical performance but also plays a vital role in promoting emotionally engaging and natural human–robot interactions, making it a promising approach for affective HRI applications. Full article

Intelligent lower-limb exoskeleton rehabilitation robots are increasingly superseding traditional rehabilitation equipment, making them a focus of research in this field. However, existing systems remain challenged by dynamic instability resulting from various disturbances during actual walking. To address this limitation, this study proposes a novel dynamic stability criterion. Through an analysis of the principles and limitations of the traditional zero-moment point (ZMP) stability criterion, particularly during the late single-leg support phase, a new stability criterion is introduced, which is founded on the swing projection polygon during single-leg support. This approach elucidates the variation patterns of the stability polygon during a single-step motion and facilitates a qualitative analysis of the stability characteristics of the human–robot system in multiple postures. To further enhance the stability and smoothness of gait trajectories in lower-limb exoskeleton rehabilitation robots, the shortcomings of conventional gait planning approaches, namely their non-intuitive nature and discontinuity, are addressed. A recurrent gait planning method leveraging Long Short-Term Memory (LSTM) neural networks is proposed. The integration of the periodic motion characteristics of human gait serves to validate the feasibility and correctness of the proposed method. Finally, based on the recurrent gait planning method, the dynamic stability of walking postures is verified through theoretical analysis and experimental comparisons, accompanied by an in-depth analysis of key factors influencing dynamic stability. Full article

Background: Myogenous temporomandibular disorders (TMDs) typically present with pain but without obvious restriction of mandibular motion, making subtle dysfunctions difficult to detect clinically. In this study, we evaluated mandibular kinematics in myogenous TMDs using an electronic axiography system (Cadiax Diagnostic). The specific objective of this study was to evaluate whether patients with myogenous temporomandibular disorders exhibit qualitative abnormalities in mandibular movements that are not detectable using conventional clinical examination. Methods: Twenty-six patients with myogenous TMD (muscle pain without intra-articular disorders, diagnosed per DC/TMD) and 26 matched controls were examined. Clinical assessment (DC/TMD Axis I) measured mandibular range of motion and deviations. Instrumental recordings of maximal opening, protrusion, and laterotrusion were obtained with Cadiax 4. Quantitative (excursion ranges) and qualitative (movement symmetry and sagittal deviations) parameters were analyzed. Condylar position changes between the reference position and maximum intercuspation were evaluated (Condyle Position Measurement, CPM). Exact χ² or Fisher tests were applied with effect sizes (φ) and 95% confidence intervals (CI). Results: Maximal opening, lateral excursions, and protrusion ranges were statistically similar between groups (mean opening: 47.96 ± 6.5 mm in TMDs vs. 49.46 ± 5.4 mm in controls, p = 0.40; 95% CI of difference −1.8 to 4.8 mm). However, qualitative deviations were more frequent in TMD. Of note, 12/26 (46.2%) patients vs. 6/26 (23.1%) controls showed a ΔY deflection during protrusion (χ² = 3.06, p = 0.08; φ ≈ 0.24; difference = 23.1%, 95% CI −2.0–48.2%). Identical proportions (46.2% vs. 23.1%) showed a ΔY deflection upon opening (χ² = 3.06, p = 0.08). Inferior condylar shifts (distractions) on closing into intercuspation occurred only in the mTMD group: 5/26 (19.2%) left condyles vs. 0% (p ≈ 0.05; 95% CI diff 4.1–34.4%) and 2/26 (7.7%) right vs. 0% (p ≈ 0.49; 95% CI −2.5–17.9%). Condylar compressions (superior shifts) were similar between groups. In summary, roughly half of TMD patients exhibited lateral jaw deflections (ΔY) and exclusive condylar “distraction” on closure; upon comparison, these conditions were rare in controls. Conclusions: Despite normal mandibular range of motion, patients with myogenous TMDs exhibited qualitative abnormalities in jaw kinematics, including movement deflections, condylar asymmetries, and centric–intercuspal discrepancies. Axiographic analysis with Cadiax enabled detection of subtle functional changes not identifiable in routine examinations, underscoring its diagnostic value in early dysfunction and potential therapeutic planning. The detection of kinematic abnormalities could influence early diagnosis or treatment planning for myogenous TMDs. Full article