Search Results (887)

Human squat motion is commonly analyzed using inverse dynamics, where joint moments are computed from experimentally measured kinematics. Such analyses typically assume that the observed motion is mechanically feasible and do not explicitly account for limitations of joint moment capacity. In this study, a computational framework is proposed for the load-constrained reconstruction of squat motion that integrates kinematic motion generation with a mechanical model of moment-limited joints. The human body is represented as a multi-segment system consisting of feet, shanks, thighs, pelvis, and torso. Joint behavior is modeled using nonlinear rotational springs with bounded moment capacity, allowing elastic response followed by allowing bounded moment response and redistribution of mechanical demand as critical moment levels are approached. A reference squat trajectory is first generated kinematically, after which a constrained optimization problem is solved at each motion frame to obtain a mechanically admissible posture under external loading. The objective function combines trajectory tracking with joint energy contributions, while gravitational loading from a barbell applied at the shoulders introduces external work. The formulation enables automatic correction of the reference motion when joint moment limits are exceeded, resulting in mechanically admissible squat postures. Numerical examples illustrate the evolution of pelvis trajectory, torso inclination, lower-limb segment angles, and reconstructed body configurations throughout the squat cycle. The results confirm that joint moment capacity directly influences the reconstructed motion and leads to load-dependent adaptation of squat posture. Full article

Humanoid upper-limb robots are an important direction in biomimetic robotics, and inverse kinematics is a key technique for achieving human-like coordinated operation. However, existing inverse kinematics methods for bimanual trajectory tracking often suffer from high computational complexity and limited synchronization performance. To address this, this paper proposes an error-adaptive competition-based inverse kinematics (EAC-IK) approach for bimanual trajectory tracking of humanoid upper-limb robots. First, a unified modeling framework for the absolute tracking errors and synchronization errors of the two arms is established, and the end-effector task constraints are reformulated into a low-dimensional representation, thereby reducing the computational complexity of the original high-dimensional task mapping. Second, to enhance the coordination capability of bimanual operations, an error-adaptive competition mechanism is developed to regulate the weighting coefficients of the two arms online according to their error states. In addition, a virtual second-order command shaper is introduced at the joint level to reconstruct joint trajectories and suppress oscillations induced by input noise and the error-adaptive competition mechanism. Simulation and experimental results on a hyper-redundant humanoid upper-limb robot demonstrate that, compared with the zeroing neural-network-based inverse kinematics method, the proposed method achieves lower tracking and synchronization errors, as well as higher computational efficiency. In the circular trajectory-tracking experiment, the left-arm position and orientation tracking errors decrease from $1.60\times {10}^{-3}\mathrm{m}$ and $4.72\times {10}^{-3}\mathrm{rad}$ to $0.70\times {10}^{-3}\mathrm{m}$ and $0.95\times {10}^{-3}\mathrm{rad}$, respectively, while the synchronization error decreases from $1.96\times {10}^{-3}$ to $1.30\times {10}^{-3}$. In addition, the average algorithm runtime decreases from $0.82\mathrm{ms}$ to $0.63\mathrm{ms}$. Full article

This paper presents an efficient inverse kinematics (IK) solution for robotic manipulators, addressing the challenges of high computational complexity, low efficiency, and sensitivity to singularities associated with traditional methods. A data augmentation strategy is introduced, utilizing an enhanced Diffusion-TS model to generate diverse joint-angle samples and corresponding end-effector poses through forward kinematics, thereby creating a high-quality dataset. To improve real-time performance, a Temporal Convolutional Network (TCN) model is developed, optimized using the Grey Wolf Optimizer (GWO), and augmented with a probabilistic sparse attention mechanism to effectively capture key pose features. Experimental evaluations on the Jaka MiniCobo robotic arm demonstrate that the proposed method significantly reduces inference time while maintaining high accuracy, making it suitable for real-world applications that demand both speed and precision. Full article

This manuscript presents the development of an HEX platform inverse kinematics model, its numerical implementation, and experimental validation. A complete inverse-kinematics formulation is established from the geometric definition of the base and mobile joint coordinates and a z–y–x Euler rotation sequence, allowing actuator-length computation for arbitrary 6-DOF poses. The model is implemented to map the operational workspace under actuator stroke and joint-angle constraints via a two-stage deterministic search, providing dense workspace point clouds, surfaces, and quantitative translational/rotational limits for multiple stroke ranges. Experimental validation is performed on a hexapod platform controlled through an embedded inverse-kinematics layer within a cascaded position–velocity–current architecture with dual-encoder actuator feedback. For a ±25 mm actuator travel range, the experiments confirm close agreement with translation simulations with differences of the order of 2% to 3% in x, y, and z, while larger discrepancies were observed in orientation limits, i.e., the model predicts γ ≈ ±32.5° and α, β ≈ ±10–11°, whereas measurements yield γ ≈ ±30° and α,β ≈ ±14–15°, evidencing higher sensitivity of rotational capability to real mechanical and control factors. Full article

Mixed Reality (MR) teleoperation offers an intuitive interface for Human-Robot Collaboration (HRC), yet it often faces the “Embodiment Gap”—a physical and kinematic mismatch between human operators and robotic platforms. Existing MR systems primarily rely on a “direct mapping” approach, where user movements are transferred directly to the robot. This forces operators to manually adapt to robotic constraints, such as singularities and joint limits, making task performance heavily dependent on individual skill. This study proposes Mixed reality Adaptive Spatial and Kinematic support (MASK), an adaptive shared-control framework designed to bridge the “Gulf of Execution” and “Gulf of Evaluation” by separating target selection from reachability and kinematic feasibility. The MASK system integrates three core modules: (1) Target Object Identification (TOI) based on body motion features to identify the intended manipulation target; (2) a Base Relocation Module (BRI) utilizing Inverse Reachability Maps to optimize the robot’s spatial configuration; and (3) a Kinematic Correction Module (KCM) that autonomously resolves kinematic constraints through pose blending and null-space optimization. Initial experimental results suggest that MASK reduces the operator’s cognitive and physical load by shifting the burden of kinematic resolution from the human to the system. This approach enables high-precision manipulation through an intuitive interface, potentially reducing the performance gap between different levels of operator proficiency. Full article

Background: Adolescent obesity and depressive symptoms have increased concurrently, often presenting as co-occurrence. However, evidence on the timing of physical activity (e.g., weekday vs. weekend) and adherence to planetary health diets remains limited. This study examined these associations among adolescents in Zhejiang Province from 2022 to 2024. Methods: A total of 261,495 adolescents aged 11–18 years were included. Physical activity (PA) and dietary behaviors were assessed through the China Common Disease and Risk Factor Surveillance among Students questionnaire (reliability: Cronbach’s α = 0.84, validity: RMSEA = 0.07). The plant-based Planetary Health Diet Index (PHDI-green) adherence was defined as consuming at least one daily serving of both vegetables and fruits. Depressive symptoms were measured using the Center for Epidemiologic Studies Depression (CES-D) scale, and co-occurrence was defined as the coexistence of obesity and depressive symptoms. Temporal trends were tested using χ² tests. Sex-stratified logistic regression, restricted cubic spline analyses, and population attributable fraction (PAF) analyses were applied. Results: From 2022 to 2024, obesity (p for trend = 0.013) and depressive symptoms (p for trend = 0.003) increased significantly, while co-occurrence remained stable (p for trend = 0.058). Boys had higher obesity and co-occurrence, whereas girls showed higher depressive symptoms (all p < 0.001). Higher weekly PA, greater weekend PA and PHDI-green adherence were associated with reduced odds of obesity in both sexes (all p < 0.001). Weekend PA showed stronger associations with depressive symptoms among girls, while PHDI-green showed stronger inverse associations in boys (p for sex difference < 0.001). PAF analyses suggested that low weekend PA accounted for substantial proportions of cases (girls: obesity 10.17%, depressive symptoms 31.30%, co-occurrence 35.64%). Joint adherence to adequate PA and PHDI-green conferred the lowest odds of co-occurrence (boys: OR = 0.40, 95% CI: 0.34–0.46; girls: OR = 0.33, 95% CI: 0.26–0.43). Conclusions: Adherence to the Planetary Health Diet may be particularly relevant for boys, whereas PA—especially weekend PA—may be more strongly associated with health outcomes among girls. These findings suggest the importance of sex-specific and time-targeted behavioral strategies for obesity, depressive symptoms, and their co-occurrence in adolescents. Full article

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines. Full article

Background/Objectives: Frailty is common among rural Chinese older adults despite relatively high daily physical activity, a phenomenon known as the “rural frailty paradox.” Conventional moderate-to-vigorous physical activity (MVPA) metrics rely on absolute cut-points and are often highly correlated with activity volume, limiting their ability to distinguish the roles of activity volume and activity intensity distribution. We therefore applied a cut-point-free accelerometer approach using average acceleration (AvAcc) and intensity gradient (IG) to distinguish activity volume from activity intensity distribution and to examine whether activity intensity distribution, together with diet quality, could help explain the rural frailty paradox beyond total activity volume alone. Methods: In this cross-sectional analysis of the Healthy Aging and Lifestyle Enhancement study, 1203 rural older adults were included. Physical activity (PA) was objectively measured using triaxial accelerometers to derive AvAcc and the IG. Diet quality was assessed using the China Prime Diet Quality Score (CPDQS), and frailty was assessed using the Fried frailty phenotype adapted for rural Chinese older adults. Multiple linear regression, joint effect models, and restricted cubic spline analyses were conducted after adjustment for age, sex, chronic disease status, total energy intake, and related covariates. Results: In mutually adjusted models, higher IG and CPDQS were independently associated with lower frailty scores, whereas AvAcc was not. In the fully adjusted model, IG (β = −0.14, p < 0.001) and CPDQS (β = −0.10, p < 0.001) were inversely associated with frailty score, while AvAcc showed no significant association (p = 0.665). In joint analyses, compared with the low-IG/low-CPDQS group, participants with high IG/high CPDQS had the lowest frailty scores (β = −0.28, p < 0.001), followed by those with low IG/high CPDQS (β = −0.20, p = 0.002). Restricted cubic spline analyses indicated a non-linear association between IG and frailty and an approximately linear inverse association for CPDQS. Conclusions: These findings suggest that, among rural older adults, frailty may be more strongly associated with activity intensity distribution than with total activity volume alone. Together with diet quality, this may help explain the rural frailty paradox. Full article

Accurate inverse kinematics for rehabilitation robotic arms remains challenging because of strong nonlinearity, multiple feasible joint configurations, and strict joint-limit constraints. Inspired by the cooperative construction, adaptive exploration, and collective information-sharing behaviors of beavers, this study develops an improved biomimetic beaver behavior optimizer (IBBO) for optimization-based inverse kinematics solving. In the proposed framework, biologically inspired cooperative search is translated into an engineering-oriented numerical strategy through four complementary mechanisms: a strict elitist replacement with rollback to preserve population fitness consistency, a momentum-inspired information transfer scheme to accumulate effective search directions, a lightweight memetic coordinate-wise local search to strengthen late-stage exploitation, and an adaptive builder–disturbance schedule to progressively shift the search from exploration to refinement. The optimization capability of IBBO is first evaluated on the CEC2017 benchmark suite, where it demonstrates competitive accuracy and robustness. It is then applied to inverse kinematics solving for representative rehabilitation robotic arms by minimizing pose errors under joint constraints. The experimental results show that IBBO can consistently generate feasible joint solutions with improved terminal pose accuracy and stable convergence compared with baseline metaheuristics. Beyond numerical improvement, this study provides a biomimetic optimization framework that transfers beaver-inspired cooperative behaviors into rehabilitation robotics, offering an effective computational approach for constrained inverse kinematics problems. Full article

Postseismic deformation observed by terrestrial Global Navigation Satellite System (GNSS) and seafloor GNSS-Acoustic techniques (GNSS-A) provides essential constraints on the depth-dependent viscoelastic structure of subduction zones. In this study, we collect and process decadal postseismic observations following the 2011 Tohoku-oki M_w9.0 earthquake, including 232 onshore GNSS stations and six offshore GNSS-A sites. After removing the interseismic velocity terms, we extract the postseismic deformation signals mainly driven by viscoelastic relaxation during the period from 3 to 9 years after the earthquake. The inversion is primarily constrained by horizontal displacements, which have higher accuracy than vertical observations. We adopt a radially layered viscoelastic Earth model with lateral heterogeneity between continental and oceanic domains based on the Burgers rheology and half-space dislocation theory. Using the least-squares principle, we invert for the optimal viscoelastic structure under the strong constraint of fixed mantle viscosity. The optimal continental and oceanic crustal elastic thicknesses are 24.4 km and 37 km, with minimum horizontal Root-Mean-Square errors (RMS) of 5.68 cm and 6.81 cm, respectively. The mantle viscosity shows significant depth-dependence and obvious land–ocean differences. These results verify the critical role of joint land and seafloor geodetic constraints and provide a refined viscoelastic structure model for subduction zones. Full article

Background: Obesity remains a major public health challenge, with central and visceral fat distribution conferring particularly high cardiometabolic risk. Lifestyle factors, including diet, physical activity, and sleep quality, have been implicated in adiposity, yet their combined and interactive effects in working populations remain insufficiently characterized. Methods: We conducted a cross-sectional study in 88,343 Spanish employees (53,122 men, 35,221 women) attending occupational health examinations between 2021 and 2024. Obesity was assessed using four complementary indices: body mass index (BMI), waist-to-height ratio (WHtR), Clínica Universidad de Navarra–Body Adiposity Estimator (CUN-BAE), and Metabolic Score for Visceral Fat (METS-VF). Lifestyle factors included sleep quality (Pittsburgh Sleep Quality Index), Mediterranean diet adherence (MEDAS), and physical activity (IPAQ). Multivariable logistic regression models were adjusted for sociodemographic and lifestyle variables, with interaction, stratified, joint exposure, and dose–response analyses. Results: Obesity prevalence varied widely by index, ranging from 18.9% (BMI) to 55.6% (CUN-BAE). Poor sleep quality was independently associated with higher odds of obesity across all indices, particularly central obesity (WHtR OR 1.58, 95% CI 1.48–1.69), with stronger associations observed in women. Physical inactivity and non-adherence to the Mediterranean diet were robust predictors, with inactivity showing the largest effect sizes (METS-VF OR 9.92, 95% CI 8.70–11.15). Interaction analyses indicated that both Mediterranean diet adherence and regular physical activity attenuated the adverse association between poor sleep and obesity outcomes. Restricted cubic spline models revealed a progressive dose–response relationship between increasing PSQI score and central obesity. Joint exposure analyses showed nearly five-fold higher odds of central obesity among workers with concurrent poor sleep, physical inactivity, and low Mediterranean diet adherence. A graded inverse association was observed between a composite healthy lifestyle score (0–3) and obesity, with a score of 3 associated with 72–75% lower odds of BMI-obesity and WHtR-high. Conclusions: In this large occupational cohort, poor sleep quality, physical inactivity, and low Mediterranean diet adherence emerged as independent and combined determinants of general, central, and visceral obesity. Integrated workplace strategies promoting sleep hygiene, physical activity, and dietary quality—particularly among women and lower socioeconomic groups—may represent an effective approach to reducing obesity risk in working populations. Full article

Humanoid manipulators with kinematic redundancy offer enhanced dexterity and adaptability to complex environments. Solving their inverse kinematics (IK) is fundamental to trajectory tracking, motion planning, and real-time control. Conventional Jacobian-based iterative methods are widely used, but they are often sensitive to the initial guess, computationally expensive, and less effective in handling strict constraints. Arm-angle-based analytical parameterization reduces redundancy resolution to a single parameter. However, joint limits may lead to multiple disconnected feasible arm-angle intervals. Many existing methods still depend on a numerical search or intelligent optimization to select the arm angle. This lowers computational efficiency and provides less explicit control over branch and configuration selection. To address these issues, this paper extends the arm-angle analytical IK framework. It introduces global configuration parameters to explicitly control the shoulder-elbow-wrist configuration. It also completes the analytical derivation of the rotational relationships of the first three joints in the reference plane. In addition, a feasibility determination and modeling scheme for the arm-angle domain is established, which covers disconnected feasible intervals. The IK problem is then reformulated as a one-dimensional optimization over the feasible domain. An efficient interval-based search is employed to determine the optimal arm angle. Experimental results demonstrate high accuracy and interference-free trajectory tracking. Comparative tests on randomly sampled target poses are also performed. The results show more concentrated error distributions, shorter average computation time, and higher success rates. These results confirm the advantages of the proposed method in accuracy, robustness, and real-time performance. Full article

This paper presents a low-cost, lightweight wearable sensing module for real-time multi-degree-of-freedom motion analysis, which is validated using ankle movements from a representative case study. The system is based on a compact inertial measurement unit integrated into a custom-made enclosure and employs Kalman filter-based sensor fusion to estimate three-dimensional joint orientation. An experimental campaign involving sixteen healthy participants was conducted, and measurements were compared against a gold-standard optical motion capture system, Optitrack V120 Trio. Ankle kinematics were analysed across all anatomical planes, including dorsiflexion/plantarflexion, inversion/eversion, and adduction/abduction. Quantitative metrics, including cosine similarity consistently above 0.98 across all movements and root mean square error within 4° on average, demonstrate strong agreement between the angular measuring device and motion capture data, with errors remaining within clinically acceptable limits. The results confirm the feasibility of the proposed system as a reliable, portable, and affordable alternative to laboratory-based measurement technologies. Beyond ankle assessment, the sensing approach is applicable to a wide range of motion-assistive and rehabilitation systems, supporting continuous monitoring, personalised therapy, and future integration into intelligent wearable devices. Full article

Background: Human lead exposure is a multi-pathway phenomenon that integrates internal biological burden with persistent residential environmental reservoirs. Although individual lead metrics have been linked to cardiometabolic dysfunction, current research often fails to capture the ‘exposome’ reality of joint, nonlinear, and interaction-dependent effects on metabolic outcomes like BMI. Objectives: To evaluate associations between biological (blood and urinary) and residential dust (window and floor) lead measures and BMI, and to characterize nonlinear and interaction-dependent mixture effects using Bayesian Kernel Machine Regression (BKMR). Methods: We analyzed data from NHANES 2001–2002, a nationally representative survey of the U.S. noninstitutionalized civilian population. Window and floor dust lead (µg/ft²) were obtained from the NHANES household dust component, and blood lead (µg/dL) and urinary lead (µg/L) were measured using standardized NHANES laboratory protocols. BMI was calculated from measured height and weight. Missing data were addressed using multivariate imputation by chained equations. Descriptive statistics and multivariable linear regression were used to estimate adjusted associations between individual lead metrics and BMI, controlling for age, gender, income, race/ethnicity, and education. BKMR was then applied to evaluate joint mixture effects, estimate univariate and bivariate exposure–response functions, and quantify relative exposure importance using posterior inclusion probabilities (PIPs). Results: In covariate-adjusted linear regression, blood lead (β = −0.485; 95% CI: −0.566, −0.405; p < 0.001) and window dust lead (β = −0.00047; 95% CI: −0.00067, −0.00026; p < 0.001) were inversely associated with BMI, whereas floor dust lead was positively associated (β = 0.258; 95% CI: 0.209, 0.306; p < 0.001). Urinary lead was inversely but not significantly associated with BMI (β = −0.111; 95% CI: −0.235, 0.013; p = 0.079). In BKMR, blood lead was the dominant contributor, with a posterior inclusion probability (PIP; proportion of iterations in which an exposure is selected) of 1.00. Window dust lead showed modest inclusion (PIP = 0.26), whereas urinary and floor dust lead were not selected (PIP = 0.00). Exposure–response functions indicated modest nonlinearity for blood lead and greater divergence for the blood lead–window dust lead pairing at higher exposure levels. The overall mixture effect declined across increasing joint exposure quantiles, crossing the null near the median and becoming increasingly negative at higher mixture levels. Conclusions: In our study, lead metrics showed heterogeneous associations with BMI, and BKMR indicated that internal lead burden (blood lead) primarily drove mixture-related BMI patterns, with evidence that window dust lead may modify mixture effects at higher co-exposure levels. These findings support evaluating multiple lead exposure pathways jointly and using flexible mixture models to capture nonlinear and interaction-dependent relationships with BMI. Full article

Background/Objectives: Prosthesis optimization after transfemoral amputation is often guided by clinical experience, yet quantitative evidence isolating how prosthesis mass, inertial properties, and alignment affect mechanical load transmission remains limited. Musculoskeletal modeling can be used as a controlled framework for examining relative sensitivity rankings of constraint force transmission across prosthetic junctions under fixed gait inputs. Methods: A model was modified to incorporate a transfemoral prosthesis. Experimental walking data from a healthy adult reference subject (Qualisys motion capture, synchronized AMTI force plates) provided kinematics and ground reaction forces for model scaling, inverse kinematics, and loading. These inputs provided a standardized mechanical reference and were not intended to represent transfemoral amputee gait. Prosthesis mass (2.625, 3.50, 4.375 kg), inertia (0.5×, 1.0×, 1.5×), and mediolateral alignment (−10, 0, +10 mm) were varied while keeping kinematics and ground reaction forces identical across conditions. Constraint reaction forces at the socket–residual limb junction and prosthetic ankle were computed and normalized to body weight. Results: Increasing mass produced the largest monotonic increases in peak resultant constraint reactions, most prominently at the socket-level junction (8.51 → 10.48 → 12.29 BW), with smaller changes at the ankle and unchanged peak timing. Inertia caused joint-specific effects, whereas mediolateral alignment minimally affected constraint reaction forces and redistributed force components. Conclusions: This study quantified the one-factor-at-a-time effects of prosthesis mass, inertia, and mediolateral alignment on inter-segment constraint reaction forces. The reported reactions should be interpreted as net rigid-body constraint reactions under fixed inputs, not as physiological joint contact forces or direct interface loads. Full article