Search Results (237)

Background/Objectives: Promoting postural health in children requires not only adequate knowledge but also the implementation of health-promoting behaviors in the school environment. Teachers play a key role in this process; however, the extent to which their knowledge is reflected in everyday practice remains unclear. The study aimed to analyze and compare the levels of knowledge among preschool, early school, and physical education teachers regarding postural health in children and adolescents, including postural abnormalities, ergonomics, the selection of corrective exercises, and behaviors that promote correct body posture. Methods: A cross-sectional study was conducted on a sample of 153 teachers in Poland: 24 preschool (P), 53 early school education (EE), and 76 physical education (PE) teachers. The self-report Teachers’ Body Posture Literacy Questionnaire (TBPLQ) was used to assess knowledge regarding postural abnormalities. Results: PE achieved the highest TBPLQ scores, with significant differences observed mainly in comparison with EE (r = 0.30–0.50, p < 0.001). Across all groups, teachers performed best in recognizing postural abnormalities and worst in selecting appropriate corrective exercises. Although knowledge levels were relatively high, only weak correlations were found between knowledge and postural hygiene-promoting behaviors. The largest behavioral differences concerned the use of appropriate sportswear during physical education classes (η² > 0.14). Conclusions: Teachers demonstrated relatively high levels of knowledge regarding posture health. However, a clear knowledge–behavior gap was identified. Knowledge was only partially translated into proactive health-promoting actions, particularly regarding corrective interventions and communication with parents. The results suggest the need for educational initiatives for teachers focusing on proactive health-promoting and postural hygiene behaviors. Full article

The implementation of wearable systems in postural rehabilitation offers new perspectives for continuous monitoring, yet their success depends on the interaction between technical parameters and clinical requirements. This study analyzes clinical performance factors to identify strategic levers determining recovery effectiveness. It examines indicators such as postural deviation detection, sensitivity to minor motion changes, suitability for home monitoring, continuous monitoring capability, clinical relevance of extracted parameters, and the capability to assess patient progress over time. Using the DEMATEL methodology, the study highlights influences among these factors in a clinical context. This structural analysis separates primary drivers from rehabilitation outcomes. To refine the analysis, the MICMAC method classifies factors by driving and dependence power, distinguishing determinant, relay, dependent, and autonomous variables. The approach provides an objective basis for managers and designers to prioritize resources toward functionalities with the greatest systemic impact on patient progress. The combined DEMATEL–MICMAC framework enhances decision-making by linking causal relationships with clear hierarchical categorization. The findings may support the integration of wearable technologies into rehabilitation practice by identifying the clinical performance factors with the strongest influence within the system. Full article

Background: Youth soccer players are exposed to repeated unilateral loading during a period of rapid growth and neuromuscular maturation. These demands may contribute to postural deviations and inter-limb functional asymmetries that can influence movement control and mechanical efficiency. This study protocol aims to establish a standardized multimodal framework for assessing postural alignment, postural control, lower limb mechanical output, ankle dorsiflexion strength, support-limb neuromuscular activation, and contextual training and recovery variables in licensed youth soccer players aged 13 to 17 years. Methods: This prospective observational study will include 75 male youth soccer players recruited from S.C. Fotbal Club Ripensia Timișoara S.A. The primary outcome is the inter-limb asymmetry index derived from unilateral countermovement jump performance. Secondary outcomes include postural alignment, balance, bilateral jump performance, ankle dorsiflexion strength, and support limb electromyographic activity during the instep kick. Participants will complete a clinical evaluation questionnaire, including demographic, training, and recovery variables. Assessments will be conducted using the GaitON system, Kinvent K-Delta force platforms, K-Myo surface electromyography, and K-Pull dynamometry, before and after a regular training session. Biological maturation will be estimated using the Mirwald maturity offset method. Expected Results: The protocol will allow characterization of inter-limb asymmetries across postural, balance, jump, and electromyographic parameters. Conclusions: This protocol aims to provide a practical and standardized model for functional screening in youth soccer players. Full article

Archery is a fine-grained skill sport in which small posture deviations can markedly affect performance, motivating the need for reliable automated technique assessment. However, most existing methods still focus on large-amplitude sports and cannot match coach-level nuance. To overcome these limitations, we introduce SEMA (Semantic Evidence-Driven Multimodal Assessment), a large language model (LLM)-based end-to-end system for fine-grained archery action quality assessment. Beyond score prediction and evaluation-text generation, SEMA further supports knowledge-grounded question answering and feedback generation through a hierarchical multi-source knowledge framework that integrates assessment outputs, structured coaching guidance, and general archery knowledge. Experimental results show that SEMA achieves strong performance on the novel AAV dataset, outperforming general-purpose VLMs and adapted prior AQA methods. In addition, we introduce the AAV (Archery Action Video) dataset, the first multimodal, fine-grained action quality assessment (AQA) dataset dedicated to archery, and release it publicly to the community. This dataset addresses a critical gap in current benchmarks for assessing archery action quality and intelligent archery training. Full article

Background/Objectives: Spinal alignment is a key determinant of biomechanical function and postural stability, particularly during periods of growth and development. Three-dimensional (3D) surface topography offers a non-invasive method for assessing spinal posture. This study aimed to evaluate spinal alignment parameters in a mixed adolescent and adult population, to investigate sex-related differences, and to analyze biomechanical relationships between spinal components. Methods: A total of 98 participants (aged 11–45 years) underwent 3D spinal surface topography assessment. Descriptive statistics were calculated for sagittal, coronal, and rotational parameters. Group comparisons between sexes were performed using independent samples t-tests. Pearson correlation analysis and linear regression were used to assess the relationships between spinal parameters. Logistic regression analysis was conducted to identify predictors of clinically relevant rotational asymmetry (surface rotation RMS > 6°). Results: Most participants exhibited near-physiological sagittal alignment, with thoracic kyphosis and lumbar lordosis within normal ranges. However, approximately 20% demonstrated clinically relevant rotational asymmetry. Female participants showed significantly higher rotational asymmetry compared to males (p = 0.008), while sagittal parameters did not differ significantly. Strong correlations were observed between thoracic kyphosis and cervical sagittal displacement (r = 0.77). Rotational asymmetry was negatively correlated with sagittal parameters and significantly predicted coronal imbalance (β = 0.38, p < 0.01; R² = 0.21). Conclusions: 3D surface topography provides a non-invasive method for assessing external postural alignment and surface-based asymmetries. Rotational asymmetry appears to represent a relevant component of spinal imbalance and is associated with coronal deviation within a multi-planar framework. These findings support the use of integrated biomechanical assessment in the evaluation of spinal alignment. Full article

Gait impairment is a common motor manifestation of Parkinson’s disease (PD), which is also frequently accompanied by other motor abnormalities such as bradykinesia, rigidity, postural instability, and movement asymmetry. These motor impairments are closely associated with reduced mobility and increased fall risk. Although wearable plantar insole sensing provides a promising basis for objective gait assessment, existing studies have mainly focused on conventional time- or frequency-domain descriptors, whereas the nonlinear complexity of gait, laterality-related imbalance, and deviation from normal gait patterns remain insufficiently characterized in an integrated manner. To address this gap, this paper proposes FID-Gait, which is a three-domain fusion framework for PD identification using instrumented insole data. The framework combines automated gait-cycle segmentation with multidomain feature modeling, including a fractal domain for nonlinear gait complexity, a plantar-loading–phase imbalance (PLPI) domain for loading asymmetry and temporal disturbance, and a covariance-adjusted deviation (CAD) domain for measuring deviation from normal gait patterns. Experiments on the PhysioNet Gait in Parkinson’s Disease dataset showed that FID-Gait achieved strong discriminative performance under multiple evaluation protocols. At the gait-cycle level, the selected MLP classifier achieved an accuracy of 99.11% and an F1-score of 99.47%. At the subject level, the selected AdaBoost classifier achieved the highest accuracy of 90.22% and the best F1-score reached 93.02%. Five-fold cross-validation further supported the robustness of the proposed representation, and leave-one-subject-out evaluation provided preliminary evidence of subject-independent generalization. Overall, FID-Gait provides an effective and interpretable framework for PD gait characterization and identification in offline experimental settings. Full article

To address the issues of poor operational stability and insufficient omnidirectional leveling capability of tracked electric agricultural machinery in hilly and mountainous areas, this paper presents an electromechanical omnidirectional leveling chassis architecture based on a dual-layer independently driven architecture. Utilizing servo electric cylinders as actuators, a leveling mechanism with physically decoupled upper lateral and lower longitudinal layers was constructed. Based on this structure, a mathematical model relating the electric cylinder displacement to the platform posture was established. Furthermore, an ADAMS dynamics simulation platform was built to conduct simulation analysis and prototype experiments. The results indicate that the designed dual-layer independently driven chassis can achieve a theoretical leveling range of ±28.6° laterally and ±27.7° longitudinally, operating smoothly under the rated 25° slope condition. Dynamic tests demonstrate that when the prototype travels at 3 km/h, the residual inclination angle of the platform can be controlled within ±0.9° in 3 s. The simulation and experimental results are in high agreement, comprehensively revealing the dynamic coupling relationship among the electric cylinder displacement, platform posture, and driving thrust. The experiments verify that the electromechanical omnidirectional leveling system can accomplish adaptive leveling under slope conditions, exhibiting superior performance regarding response speed, control accuracy, and disturbance rejection, with the thrust deviation rate between simulation and experiment within 6.71%. Full article

Childhood obesity is a growing global health problem with significant biomechanical and psychosocial consequences. While many studies have examined these domains separately, few integrate postural abnormalities, psychophysical functioning, and lifestyle factors within a single framework. This narrative review synthesises the literature published between 2005 and 2025 to summarise current evidence and identify research gaps. The findings indicate that overweight and obesity increase the risk of musculoskeletal deviations such as genu valgum, flat feet, and increased lumbar lordosis, as well as altered gait biomechanics and reduced motor competence. Excess body weight is also associated with lower self-esteem, negative body image, depressive symptoms, and reduced health-related quality of life in children and adolescents. These outcomes appear to be influenced by modifiable lifestyle factors, including parental health behaviours, sleep patterns, and screen time, although reported associations remain inconsistent. Notably, few studies address biomechanical, psychological, and environmental factors simultaneously, which limits the understanding of their interactions. To address this gap, a prospective observational study of 250–300 children aged 7–17 years is proposed. The study will combine objective postural assessments, validated psychometric tools, and lifestyle analyses at baseline and after a 12–14-month follow-up. This integrated approach aims to identify postural compensation patterns, psychosocial risk trajectories, and modifiable behavioural predictors associated with childhood obesity, supporting the development of early preventive and interdisciplinary interventions. Full article

Traditional deadlift guidelines prioritize maintaining a neutral spine to prevent low back injuries. However, recent evidence questions whether moderate spinal flexion under load is inherently harmful, especially among trained individuals. This article proposes a modern, multifactorial framework for deadlift-related injury prevention that moves beyond rigid postural prescriptions. It integrates biomechanical evidence, load management strategies, movement variability principles, and dynamic trunk control. This narrative review synthesizes literature identified through structured searches of PubMed, Scopus, and Google Scholar, prioritizing peer-reviewed studies examining spinal biomechanics, load management, motor control, and injury epidemiology. Evidence suggests that trained lifters often exhibit natural lumbar flexion without clear prospective evidence of increased injury risk. Abrupt increases in training load appear to be consistently associated with elevated injury incidence, although relationships remain probabilistic and context-dependent. While technical factors, including spinal posture, may influence local tissue loading, current evidence suggests that rapid changes in training exposure and cumulative load management appear to be more consistent predictors of injury risk than isolated deviations from an externally defined “neutral” alignment. Movement variability appears protective, and dynamic trunk control is more functionally relevant than static core strength. A paradigm shift is needed in how deadlifts are coached and programmed. Injury prevention should emphasize progressive loading, adaptive movement strategies, and dynamic stability, rather than rigid technique enforcement. Rather than systematically appraising all available evidence, this review offers an interpretative synthesis to guide modern, evidence-informed coaching and rehabilitation practice. Full article

In response to the redundant spatio-temporal modeling and insufficient adaptation to dynamic decision-making in eye-tracking interaction of smart home interfaces, a smart home interface eye-tracking response optimization model based on spatio-temporal Transformer and gate control cross-attention is proposed. It adapts the physiological characteristics of eye-tracking jumps through dynamic sparse attention gating to compress computational redundancy and combines multi-objective reinforcement learning attention modulation to construct a closed-loop decision-making mechanism, optimizing interface parameters in real-time. Experiments showed that the model reduced eye-tracking trajectory prediction error by 23.7% compared to advanced benchmarks, increased the success rate of adapting to dynamic mutation scenarios to 89.2%, and controlled performance fluctuations within 2.3% under noise interference. In high-fidelity user testing, the accuracy of cross-task gaze transfer reached 93.4%, the failure rate of glare interference was optimized to 2.4%, and the user cognitive load index was reduced by 27.9%. Its resource consumption and energy consumption were reduced by 26.7% and 44.9%, respectively, while its posture deviation tolerance remained at 3.5°. The sparse spatio-temporal modeling of the spatio-temporal adaptive Transformer module and the enhanced gating mechanism of the hierarchical gated cross-attention module work together to break through the limitations of traditional methods in computational efficiency and dynamic feedback, providing high-precision and low-latency eye-tracking interaction solutions for smart home interface systems, and promoting the practical evolution of personalized human–machine collaborative control. Full article

Accurate trajectory tracking is challenging for tracked agricultural vehicles in orchards. Uneven terrain, track slip, and vehicle posture variations are the main causes, often leading to model mismatch and degraded control performance. To address these issues, this paper proposes an improved nonlinear model predictive control (NMPC) strategy integrated with curvature feedforward compensation for trajectory tracking of tracked agricultural vehicles under uneven terrain conditions. An enhanced kinematic model based on the instantaneous center of rotation is developed by incorporating vehicle roll and pitch angles, and track slip parameters are estimated online using a Levenberg–Marquardt optimization method to improve prediction accuracy. Furthermore, curvature feedforward information derived from the reference trajectory is embedded into the NMPC objective function to provide anticipatory control inputs and reduce computational burden. Simulation results demonstrate that compared to conventional NMPC, the proposed method reduces the mean and standard deviation of tracking error by 30.28% and 32.46% respectively, while decreasing the mean and standard deviation of heading error by 37.27% and 35.05%. Concurrently, the maximum of optimize solution time is significantly reduced, effectively resolving tracking accuracy degradation caused by system solution timeouts. Field experiments conducted under different load conditions further validate that the proposed control strategy significantly reduces lateral, longitudinal, and heading tracking errors compared with conventional NMPC, confirming its effectiveness and robustness for tracked agricultural vehicle trajectory tracking in complex orchard environments. Full article

Background: Wearable sensors have gained increasing popularity as an objective method for remotely monitoring infant movement in naturalistic settings. Over the first year of life, infants generate a wide range of motions, from goal-directed to spontaneous movement. These include linear movements, such as kicks, and orientation changes, such as postural transitions. Many sensor processing pipelines emphasize capturing linear movements through movement-generated acceleration while focusing less on information about orientation embedded in the gravitational part of the data. Here, we introduce a complementary gravity-referenced approach that extracts the gravitational component of accelerometer signals to estimate limb orientation, extending the reliable quantification of rich and detailed aspects of infant movement. Infant orientation has demonstrated clinical relevance, including associations with later neuromotor outcomes, and it can be used to chart infant motor development, motivating the development of objective methods to quantify orientation from sensor data. Methods: Wearable sensors (Opal APDM) were used to longitudinally evaluate infant motor activity recorded in sessions conducted at 3, 6, 9, and 12 months of age. We extracted data from a 5 min segment that has simultaneous video recordings. From these datasets, applying the gravity-referenced method, we computed pitch, roll, and yaw, angles that collectively describe limb orientation. We then quantified orientation variability using axis-specific circular standard deviations (SDs) for pitch, roll, and yaw and a multi-axis composite measure based on generalized variance. Results: Axis-specific circular SDs for pitch, roll, and yaw, as well as the composite generalized variance, increased significantly from 3 to 12 months (p ≤ 0.01 for each metric). Composite variability was strongly associated with Mullen gross motor outcomes at 9 and 12 months of age (r = 0.55, p < 0.001). Conclusions: Overall, gravity-referenced pitch, roll, and yaw provide rich orientation features that increased as infants develop more postural transitions. Furthermore, the orientation features correlated with standardized measures of infant motor function. These orientation metrics can complement traditional linear kinematic measures and improve our ability to granularly track infant motor development in the first year of life. Full article

Human pose estimation is a crucial task in computer vision with widespread applications in healthcare, rehabilitation, sports, and remote monitoring. In this paper, we propose a deep learning-based method for automatic human pose estimation and joint angle computation, tailored specifically for physiotherapy and telemedicine scenarios. Beyond pose estimation, the proposed method is able to compute angles between joints, enabling analysis of body alignment and posture. The proposed approach is built upon a customized skeleton with 25 anatomical keypoints and a dataset composed of over 150,000 annotated and augmented images derived from multiple open-source datasets. Experimental results demonstrate the effectiveness of the proposed method, achieving a mAP@50 of 0.58 for keypoint localization and 0.98 for object detection. Moreover, we demonstrate several real-world practical use cases in evaluating exercise correctness and identifying postural deviations by exploiting the proposed method, confirming that the proposed method can represent a promising approach for automated motion analysis, with potential impact on digital health, rehabilitation support, and remote patient care. Full article

Autonomous tractors have been shown to possess the capability to ensure a high degree of operational precision during seeding activities on flat terrain. However, in topographically challenging environments characterised by significant elevations and pronounced variations in slope, factors such as road gradients have been shown to compromise the precision of satellite-based positioning systems. This, in turn, can lead to alterations in vehicle posture and the generation of disparate longitudinal driving forces between the left and right tyres. It is important to note that this deviation from the predefined path has the potential to result in rollover accidents. Evidence has been presented that indicates a correlation between road gradient and vehicle roll motion. The proposed methodology is an algorithmic approach to the estimation of lateral slope, integrating inertial measurement unit (IMU) sensors and ground-based ultrasonic radars. This algorithmic approach is proposed as a means to achieve more accurate estimations of lateral slope. The initial development of the vehicle dynamics model was based on slope operation requirements, and the model was endowed with eight degrees of freedom. The utilisation of an unscented Kalman filter (UKF) facilitates the integration of inertial measurement unit (IMU) and ground-based ultrasonic radar measurements, thereby enabling real-time estimation of key motion states, such as lateral slope. The validity of the proposed algorithm was established through a combination of hardware-in-the-loop testing and field trials involving real tractors. The findings indicate that the implementation of this algorithm leads to a substantial enhancement in the trajectory tracking accuracy of tractors during slope operations. This enhancement is characterised by a substantial reduction in lateral deviation and an effective augmentation in the operational pass rate. In the course of empirical trials conducted in a mountainous environment, the lateral positioning deviation during straight-line driving was diminished from 10 cm to within 5 cm. Concurrently, the precision of lateral slope estimation was enhanced to 0.04 degrees. Full article

Background and Objectives: Dentofacial asymmetries are common in patients with malocclusions, while mild body postural asymmetries are frequently reported in otherwise healthy individuals. However, their interrelationship remains insufficiently investigated in adults without diagnosed spinal disorders. This study aimed to evaluate the association between dentofacial and body postural asymmetries in adults with malocclusions. Materials and Methods: A clinical cross-sectional observational study was conducted on 102 adults (18–45 years) with malocclusions and no spinal pathology. Standardized clinical morphometric examinations assessed dentofacial asymmetries (horizontal and vertical planes), dental parameters (dental midlines deviation and occlusal plane inclination), and body postural asymmetries (head, shoulder, trunk, pelvic, and lower limb alignment). Asymmetries were recorded using predefined clinical thresholds. Statistical analyses included the Wilcoxon signed-rank test, Pearson chi-square test, and Spearman’s rank correlation coefficient. Results: Dentofacial asymmetries were identified in both planes and occurred more frequently on the left side. Horizontal facial asymmetries were most common at the cheek (74.5%), nostril (66.7%), and mandibular angle levels (57.9%), and were influenced by sex, age, facial growth pattern, and facial profile (p ≤ 0.05). Mandibular dental midline asymmetry was present in 55.8% of patients. Body postural asymmetries were also frequent, particularly unilateral (60.8%) or anterior (55.9%) head inclination and shoulder asymmetries (54.9%), with a predominance on the left side and associations with age, body mass index, and postural attitude (p ≤ 0.05). Correlations were identified among facial asymmetries and among body postural asymmetries (p ≤ 0.01), indicating a bilateral distribution pattern. Additionally, right-sided facial asymmetries showed significant positive associations with right-sided body postural asymmetries (ρ = 0.197–0.229; p ≤ 0.05). Conclusions: Dentofacial and body postural asymmetries have been identified in adults with malocclusions and presented side-specific associations regarding the patterns of asymmetry. Full article