Search Results (748)

To improve understanding of anterior cruciate ligament (ACL) injuries, this study investigated the effect of menstrual cycle (MC) phase on ACL injury risk factors in elite female team sport athletes with and without urinary incontinence (UI). Additionally, associations between endogenous sex hormones, MC-related symptoms, and these risk factors were investigated. Ten elite female athletes (24.2 ± 3.6 years, BMI 23.2 ± 1.3 kg/m², 10.9 ± 1.8 training hours/week) completed three testing sessions across three MC phases, determined using the three-step method. Assessments included static and dynamic postural control and hip strength. Mixed-model ANOVA and canonical correlation analyses evaluated the effects of MC phase, UI, hormones, and performance. A significant interaction between MC phase and UI was observed for single-leg sway area with eyes closed (p = 0.036), and UI was associated with a higher hip adduction:abduction ratio (p = 0.037). No further significant interaction between UI and MC phase was observed. Moreover, hormones explained 16.5% of the variance in risk factors, while subjective symptoms explained 24.5%. Lower progesterone was associated with higher symptoms, lower estradiol and progesterone with reduced strength and poorer postural control, and higher testosterone with greater strength. Although limited by its pilot design, menstrual symptoms, more than MC phases, may influence performance and injury risk, supporting the potential value of systematic symptoms monitoring. Full article

In the era of Industry 4.0, artificial intelligence technology is experiencing rapid development, and the integration of artificial intelligence (AI) with flexible sensors has emerged as a transformative approach for human motion and posture sensing. This paper explores the advancements in AI-enhanced flexible sensors, focusing on the application of flexible sensors on various parts of the human body. Flexible sensors, due to their conformability and sensitivity, are ideal for capturing the dynamic and subtle movements of the human body. AI algorithms, particularly machine learning and deep learning techniques are employed to process the complex data streams from these sensors, enabling the accurate recognition and prediction of various human postures and motions. The combination of these technologies overcomes the limitations of traditional sensing systems, offering higher precision, adaptability, and real-time feedback. It can be applied to healthcare for rehabilitation monitoring, sports for performance enhancement, and human–computer interaction for intuitive control. This review also discusses the challenges such as sensor reliability, data privacy, and power management. The future outlook emphasizes more sophisticated AI models and deeper technology integration, promising a seamless integration into everyday life for enhanced human–machine interaction and health monitoring. Full article

A safe physical human–robot interaction (pHRI) in rehabilitation requires reliable perception and low-latency decision making under heterogeneous and unreliable sensor inputs. This paper presents a multimodal sensor-fusion-based safety framework that integrates physical state estimation, semantic information fusion, and an edge-deployed large language model (LLM) for real-time pHRI safety control. A dynamics-based virtual sensing method is introduced to estimate internal joint torques from external force–torque measurements, achieving a normalized mean absolute error of $18.5\%$ in real-world experiments. An asynchronous semantic state pool with a time-to-live mechanism is designed to fuse visual, force, posture, and human semantic cues while maintaining robustness to sensor delays and dropouts. Based on structured multimodal tokens, an instruction-tuned edge LLM outputs discrete safety decisions that are further mapped to continuous compliant control parameters. The framework is trained using a hybrid dataset consisting of limited real-world samples and LLM-augmented synthetic data, and evaluated on unseen real and mixed-condition scenarios. Experimental results show reliable detection of safety-critical events with a low emergency misdetection rate, while maintaining an end-to-end decision latency of approximately 223 ms on edge hardware. Real-world experiments on a rehabilitation robot demonstrate effective responses to impacts, user instability, and visual occlusions, indicating the practical applicability of the proposed approach for real-time pHRI safety monitoring. Full article

A Lyapunov-based hybrid model predictive control (LHMPC) method is proposed for the control of a vehicle hybrid logic dynamic system (MLD) that regulates vehicle height through asymmetric damping forces. This method addresses the limitations of traditional hybrid model predictive control (HMPC), including its inability to guarantee closed-loop stability, long prediction horizons, and excessive computational burden. The method incorporates the decreasing condition of the Lyapunov function as a contraction constraint mechanism, ensuring asymptotic stability throughout the control process. Additionally, by following the terminal constraint principle, the Lyapunov function is introduced as an inequality constraint set, replacing the terminal equality constraints typically used in traditional stability frameworks. This further guarantees the recursive feasibility and closed-loop stability of the MLD system optimization. Simulation results based on a seven-degree-of-freedom vehicle model demonstrate that the proposed LHMPC significantly outperforms conventional HMPC in terms of height tracking accuracy, convergence rate, vibration suppression, and real-time controller performance. Furthermore, the method can effectively harness the vehicle body’s vibrational energy while achieving coordinated control of vehicle height and posture, thereby reducing energy consumption during the height adjustment process. Full article

Historic buildings exhibit coupled response characteristics during long-term service, characterized by slowly varying global inclination evolution superimposed with local component-level deformation. Meanwhile, multi-source measurements are susceptible to environmental noise and structural non-integrality, which poses challenges to obtaining stable and physically interpretable inclination measurements. To address these issues, this study proposes a multi-source fusion monitoring method for global inclination and local deformation of historic buildings using an extended Kalman filter with fractional-order state modeling (FEKF). A state-space model incorporating global inclination, local component-level additional deformation, and their projection relationships is established, in which global inclination information derived from Global Navigation Satellite System (GNSS) and local observations obtained from inclinometers are formulated within a unified measurement framework. Fractional-order dynamics are introduced into the state evolution model to represent the long-memory and non-stationary characteristics of structural responses in historic buildings. By adopting a finite-memory approximation, the fractional-order model is embedded into the extended Kalman filtering framework, enabling joint estimation and physical decoupling of multi-source measurements. Numerical simulation results demonstrate that the proposed method can stably separate global inclination and local deformation components under noisy conditions, while improving the stability of global inclination estimation. Further validation using measured data from a historic building shows that the fusion results effectively suppress high-frequency disturbances in GNSS measurements and allow reliable reconstruction of local component-level inclination responses, indicating good stability and practical applicability. These results demonstrate that the proposed approach provides a physically consistent and robust solution for long-term posture and deformation monitoring of historic buildings. Full article

Background: Smoking is a major public health concern worldwide and is associated with adverse effects on pulmonary function, postural control, and overall physical performance. Aquatic exercise has gained increasing attention as a safe and effective training modality due to its unique physical properties. However, evidence regarding the effects of aquatic exercise on pulmonary function and dynamic balance in young adult smokers remains limited. Objective: This study aimed to investigate the effects of an 8-week aquatic exercise training program on pulmonary function parameters and dynamic balance performance in young adult smokers. Methods: Twenty-two physically inactive male smokers were randomly assigned to an experimental group (n = 11) or a control group (n = 11). The experimental group participated in an aquatic exercise program three times per week for eight weeks, while the control group maintained their usual daily activities. Pulmonary function parameters, including FEV₁, FVC, FEV₁/FVC, PEF, PIF, MVV, VC, TV, and IVC, were assessed using spirometers. Dynamic balance performance was evaluated using a portable dynamic balance platform under single-leg (right and left) and double-leg conditions. Data were analyzed using a two-way repeated-measures ANOVA. Results: Statistically significant time × group interaction effects were observed for vital capacity (VC) (p = 0.033, η²_p = 0.378) and tidal volume (TV) (p < 0.001, η²_p = 0.734), suggesting potentially greater changes in the experimental group compared to the control group. Peak expiratory flow (PEF) demonstrated significant main effects of time (p = 0.047) and group (p = 0.031). Dynamic balance performance showed statistically significant time × group interaction effects across right-leg, left-leg, and bilateral conditions (p < 0.01), with large effect sizes (η²_p = 0.762, 0.609, and 0.507, respectively). However, given the pilot nature and limited sample size of the study, these findings should be interpreted as preliminary. No significant changes were observed in FEV₁, FVC, or FEV₁/FVC ratio. Conclusions: This pilot randomized trial suggests that an 8-week aquatic exercise program is feasible and may produce preliminary improvements in selected pulmonary function parameters and dynamic balance in young adult smokers. Larger, adequately powered trials are required to confirm these findings. Full article

This research addresses the coupling issue between speed tracking and vertical posture in articulated unmanned mining trucks within unstructured environments. An extension theory-based coordinated control strategy is proposed, incorporating both articulation joint safety and vehicle stability. The control framework employs extension theory to classify operational modes based on articulation angle and velocity deviation. For longitudinal motion, active disturbance rejection control (ADRC) is adopted to mitigate the influence of varying payload mass and road slope on speed tracking performance. For vertical dynamics, a soft actor–critic (SAC) algorithm regulates active suspension to improve ride comfort. Both simulations and hardware-in-the-loop testing results demonstrate the superiority of the proposed strategy: coordinated control maintains speed tracking error below 4%, reduces body acceleration by 16.1%, 11.9%, and 17.5%, and improves articulation angle oscillations by 12.6%, 14.6%, and 15.1% across scenarios, confirming the strategy’s enhanced performance over conventional single-loop control approaches. Full article

Traditional ergonomic considerations in human–machine and human–swarm systems have primarily relied on static diagnostic snapshots, which often fail to capture the temporal accumulation and non-linear dissipation of musculoskeletal fatigue. As Industry 5.0 transitions toward immersive, human-centric cyber-physical systems, redefining ergonomic load as an endogenous state variable allows for real-time control of musculoskeletal integrity. This work proposes the Dynamic Integrity Governor (DIG) framework, which treats ergonomic load as a normalized, dimensionless state variable $\xi \left(t\right)$ that evolves according to a stochastic proxy of recursive Newton–Euler dynamics. Leveraging a machine-perception-aware Adaptive Event-Triggered Mechanism (AETM) and the Multi-modal Flamingo Search Algorithm (MMFSA), we develop a decentralized architecture that redistributes ergonomic demands in real-time. The framework utilizes a 7-DOF kinematic model and Control Barrier Functions (CBF) to maintain human–swarm interaction within safe biomechanical boundaries, effectively filtering stochastic sensor noise through Girard-based stability buffers. Computational validation via N = 1000 Monte Carlo runs demonstrates that the proposed strategy achieves a 79.97% reduction in control updates (SD = 0.19%; p < 0.0001; Cohen’s d = 2.41), ensuring a positive minimum inter-event time (MIET) to prevent the Zeno phenomenon and supporting carbon-aware AI operations. The integration of variable prediction horizons yields an 80.69% improvement in solving time, while ensuring a minimal computational footprint suitable for real-time edge deployment. The identification of optimal postural niches maintains peak ergonomic load at 41.42%, representing a significant safety margin relative to the integrity barrier. While validated against a 50th percentile male profile, the DIG framework establishes a modular foundation for personalized ergonomic governors in inclusive Industry 5.0 applications. Full article

Understanding how seat-induced whole-body vibration (WBV) is transmitted to and actively compensated by the human body is essential for accurately assessing discomfort, fatigue, and postural control in vehicle occupants. This study proposes a visual–inertial fusion framework utilizing IMU-RGB-D data to isolate seated human body vibration in dynamic vehicular environments. In real-cabin monitoring systems, measured motion is a superposition of platform vibration, passive transmission through the body, active postural compensation, and camera jitter. Existing WBV and driver monitoring studies typically rely on single modality sensing, such as inertial or visual approaches, without decomposing these components or modelling camera vibration. The framework synchronized three IMUs with RGB-D landmarks. Seat, human body, and camera accelerations are separated, and body vibration velocity is derived from body–seat differential acceleration via band-pass filtering and spectral integration. The 3D landmarks enable rotational-translational Postural Compensation Index metrics, axis-wise energy distributions, and anthropometric consistency checks. The study is held in an in-service urban tram case. Torso vibration is dominated by 40% anteroposterior components, while head postural is predominantly > 50% lateral sway. Near static anthropometric evaluation was also studied, resulting in shoulder width errors that remain within ±10–20 mm. The results show that the framework can distinguish passive ride phases from strongly compensated phases, separate camera jitter from true body motion, and reveal anisotropic postural strategies, providing a structured basis for vibration and posture analysis in in-vehicle monitoring. Full article

Maritime search and rescue (SAR) operations are challenged by vast search areas, poor visibility, and the time-critical nature of victim survival, particularly in dynamic coastal areas. This study presents an intelligent unmanned aerial vehicle (UAV) framework for real-time detection, tracking, and prioritization of people in distress at sea. Unlike existing UAV-based SAR systems that rely on visual sensing or offline human intervention, the proposed framework integrates RGB-thermal multimodal sensing and posture recognition to enhance victim prioritization and survivability estimation. Visual-thermal data support human posture detection, inference of physiological indicators, and autonomous UAV navigation. Metadata are transmitted to a ground control station to enable adaptive altitude control, trajectory rejoining, and multi-target prioritization. Field-inspired experiments in Quang Ninh Province, Vietnam demonstrated robust real-time performance, achieving 23 FPS with detection accuracy up to 84% for swimming subjects and over 50% for drowning postures. These findings demonstrate that Edge-AI-enabled UAVs can serve as a practical and efficient solution for maritime SAR, reducing response times and improving mission outcomes. Full article

Background. Accurate assessment of lower limb alignment is critical in diagnostic decision-making for musculoskeletal disorders. This study aimed to validate the PAViR (Posture Analyzing and Virtual Reconstruction) system, a non-invasive device based on artificial vision and 2D photogrammetry, for measuring the Hip–Knee–Ankle (HKA) angle. Method. A total of sixty-one adult participants were evaluated using the PAViR system, and the results were compared against Kinovea, a validated open-source software commonly used for 2D kinematic and angular analysis in clinical and sports biomechanics. Statistical analyses included the Shapiro–Wilk test, Pearson correlation, and Bland–Altman plots. Results. The correlation between both systems was perfect (r = 0.999; p < 0.001). The Bland–Altman analysis showed differences of 0.03° (left) and 0.04° (right), with limits of agreement between −0.25° and +0.75°, within the clinically acceptable margin of ±2°. These findings demonstrate that the PAViR system shows excellent agreement with a validated 2D photogrammetry reference method for measuring the Hip-Knee-Ankle angle in asymptomatic adults. The narrow limits of agreement (−0.25° to +0.75°) and minimal systematic bias (0.03–0.04°) support the technical validity of PAViR for static coronal plane alignment assessment under controlled conditions. Conclusions. Further validation studies in clinical populations and dynamic contexts are necessary to establish broader applicability and clinical utility. Its integration could enhance lower limb assessment in orthopedic, sports, and preventive care. Further validation studies in clinical populations with musculoskeletal pathology, dynamic functional contexts, and direct comparison with radiographic gold standards are necessary to establish broader applicability and clinical utility. Full article

This paper presents RehabHub, a home-based exergaming system that integrates standardized physical assessment directly into gameplay by using a common webcam and MediaPipe for real-time pose estimation. The system quantifies upper-limb movement quality, specifically abduction, shoulder flexion, and elbow flexion based on FMA-UE guidelines, by applying Dynamic Time Warping (DTW) together with a Z-score-based scoring model that relies on data from non-clinical adult participants. A pilot study, which included movements simulated with a 5-kg resistance band, evaluated three feature-extraction methods. The findings indicate that the single-angle method provides the clearest distinction between normal and abnormal movements, particularly for abduction and elbow flexion. In the case of shoulder flexion, the score separation was less distinct because of movement variability and posture-related angle fluctuations, which suggests that further refinement of feature design is needed. The cloud-based platform supports remote monitoring and gives caregivers access to both performance scores and recorded exercise videos. Overall, the results demonstrate the feasibility of a low-cost webcam-based assessment integrated into exergaming, and they highlight important trends for improving abnormal-movement detection in home rehabilitation systems. Full article

The margin of stability (MoS) is a metric used to assess dynamic postural stability during walking. Although MoS is typically computed from optical motion capture data, previous studies have shown that MoS can be approximated from six-axis kinematic signals—linear acceleration and angular velocity—measured by inertial measurement units (IMUs). With IMU-equipped devices such as smartphones and smartwatches becoming widespread, it is increasingly common for individuals to carry two or more such devices in daily life. This study aimed to identify combinations of two body locations that most effectively predict MoS. IMU sensors were attached to ten body locations while participants walked on a treadmill. Principal motion analysis, a reductive regression method for multidimensional time-series data, was employed for MoS prediction, and cross-validation was used for reliable model evaluation. Appropriate combinations of two IMU sensors achieved mean errors of approximately 30 mm and 11 mm in anterior and mediolateral MoS, respectively, compared with reference values derived from optical motion capture. These errors were comparable to the intrinsic standard deviations of MoS, suggesting that IMU-based MoS estimation is sufficiently accurate for the classification of individuals at high fall risk. Full article

Background: Rapid helicopter air ambulance (HAA) response is a cornerstone of emergency medical logistics, yet the “time-to-care” metric remains highly sensitive to uncertainties in base posture, readiness, and operational disruptions. This study evaluates how these factors jointly influence response-time reliability and identifies strategies for improving service performance. Methods: A Monte Carlo simulation was developed to model the end-to-end HAA mission chain, including dispatch, wheels-up delay, en-route flight, and patient handoff, while accounting for uncertainty from weather, airspace congestion, and flight dynamics. Scenario experiments incorporated training improvements and alternative response protocols (Ground vs. Airborne Standby). Results: Simulation results indicate that operational factors reduced mean and tail response times, with Airborne Standby reducing the probability of exceeding a 45 min threshold by over 90% in urban night scenarios. Performance gains were most prominent in rural service areas and night operations, where disruption risks were highest. Conclusions: The findings offer evidence-based guidance for EMS logistics planners by clarifying how standby policies and readiness enhancements mitigate logistical risks. Full article

Axial postural abnormalities in Parkinson’s Disease (PD) are traditionally assessed using clinical rating scales, although picture-based assessment is considered the gold standard. This study evaluates the reliability and clinical relevance of two markerless body-tracking frameworks, the RGB-D-based Microsoft Azure Kinect (providing the reference KIN_3D model) and the RGB-only Google MediaPipe Pose (MP), using a synchronous dual-camera setup. Forty PD patients performed a 60 s static standing task. We compared KIN_3D with three MP models (at different complexity levels) across horizontal, vertical, sagittal, and 3D joint angles. Results show that lower-complexity MP models achieved high congruence with KIN_3D for trunk and shoulder alignment (ρ > 0.75), while the lateral view significantly improved tracking of sagittal angles (ρ ≥ 0.72). Conversely, the high-complexity model introduced significant skeletal distortions. Clinically, several angular parameters emerged as robust metrics for postural assessment and global motor impairments, while sagittal angles correlated with motor complications. Unexpectedly, a more upright frontal alignment was associated with greater freezing of gait severity, suggesting that static postural metrics may serve as proxies for dynamic gait performance. In addition, both RGB-only and RGB-D frameworks effectively discriminated between postural severity clusters. While the higher-complexity MP model should be avoided due to inaccurate 3D reconstructions, our findings demonstrate that low- and medium-complexity MP models represent a reliable alternative to RGB-D sensors for objective postural assessment in PD, facilitating the widespread application of objective posture measurements in clinical contexts. Full article