Search Results (1,950)

Motion capture (MoCap) data derived from wearable Inertial Measurement Units is essential to applications in sports science and healthcare robotics. However, a significant amount of the potential of this data is limited due to missing data derived from sensor limitations, network issues, and environmental interference. Such limitations can introduce bias, prevent the fusion of critical data streams, and ultimately compromise the integrity of human activity analysis. Despite the plethora of data imputation techniques available, there have been few systematic performance evaluations of these techniques explicitly for the time series data of IMU-derived MoCap data. We address this by evaluating the imputation performance across three distinct contexts: univariate time series, multivariate across players, and multivariate across kinematic angles. To address this limitation, we propose a systematic comparative analysis of imputation techniques, including statistical, machine learning, and deep learning techniques, in this paper. We also introduce the first publicly available MoCap dataset specifically for the purpose of benchmarking missing value imputation, with three missingness mechanisms: missing completely at random, block missingness, and a simulated value-dependent missingness pattern simulated at the signal transition points. Using data from 53 karate practitioners performing standardized movements, we artificially generated missing values to create controlled experimental conditions. We performed experiments across the 53 subjects with 39 kinematic variables, which showed that discriminating between univariate and multivariate imputation frameworks demonstrates that multivariate imputation frameworks surpassunivariate approaches when working with more complex missingness mechanisms. Specifically, multivariate approaches achieved up to a 50% error reduction (with the MAE improving from 10.8 ± 6.9 to 5.8 ± 5.5) compared to univariate methods for transition point missingness. Specialized time series deep learning models (i.e., SAITS, BRITS, GRU-D) demonstrated a superior performance with MAE values consistently below 8.0 for univariate contexts and below 3.2 for multivariate contexts across all missing data percentages, significantly surpassing traditional machine learning and statistical methods. Notable traditional methods such as Generative Adversarial Imputation Networks and Iterative Imputers exhibited a competitive performance but remained less stable than the specialized temporal models. This work offers an important baseline for future studies, in addition to recommendations for researchers looking to increase the accuracy and robustness of MoCap data analysis, as well as integrity and trustworthiness. Full article

Accurate node localization is essential for many Internet of Things (IoT) applications. However, incomplete and noisy distance measurements often degrade the reliability of the Euclidean Distance Matrix (EDM), which is critical for range-based localization. To address this issue, a Low-Rank Matrix Completion approach based on nonconvex rank approximation (LRMCN) is proposed to recover the true EDM. First, the observed EDM is decomposed into a low-rank matrix representing the true distances and a sparse matrix capturing noise. Second, a nonconvex surrogate function is used to approximate the matrix rank, while the $l_1$-norm is utilized to model the sparsity of the noise component. Third, the resulting optimization problem is solved using the Alternating Direction Method of Multipliers (ADMMs). This enables accurate recovery of a complete and denoised EDM from incomplete and corrupted measurements. Finally, relative node locations are estimated using classical multi-dimensional scaling, and absolute coordinates are determined based on a small set of anchor nodes with known locations. The experimental results show that the proposed method achieves superior performance in both matrix completion and localization accuracy, even in the presence of missing and corrupted data. Full article

Exercise-induced fatigue can degrade athletic performance and increase injury risk, yet traditional fatigue assessments often rely on subjective measures. This study proposes an objective fatigue recognition approach using high-fidelity motion capture data and deep learning. This study induced both cognitive and physical fatigue in 50 male participants through a dual task (mental challenge followed by intense exercise) and collected three-dimensional lower-limb joint kinematics and kinetics during vertical jumps. A bidirectional Gate Recurrent Unit (GRU) with an attention mechanism (BiGRU + Attention) was trained to classify pre- vs. post-fatigue states. Five-fold cross-validation was employed for within-sample evaluation, and attention weight analysis provided insight into key fatigue-related movement phases. The BiGRU + Attention model achieved superior performance with 92% classification accuracy and an Area Under Curve (AUC) of 96%, significantly outperforming the single-layer GRU baseline (85% accuracy, AUC 92%). It also exhibited higher recall and fewer missed detections of fatigue. The attention mechanism highlighted critical moments (end of countermovement and landing) associated with fatigue-induced biomechanical changes, enhancing model interpretability. This study collects spatial data and biomechanical data during movement, and uses a bidirectional Gate Recurrent Unit (GRU) model with an attention mechanism to distinguish between non-fatigue states and fatigue states involving both physical and psychological aspects, which holds certain pioneering significance in the field of fatigue state identification. This study lays the foundation for real-time fatigue monitoring systems in sports and rehabilitation, enabling timely interventions to prevent performance decline and injury. Full article

Background/Objectives: Untreated dental caries, the single most common health condition globally, is strongly associated with behavioural factors. This study examined dental status and oral health habits in child–parent and sibling pairs. Methods: We retrospectively analysed records from a dental practice in northeastern Poland, including 90 child–parent pairs and 27 sibling pairs. Dental status was assessed using the Decayed-Missing-Filled Teeth (DMFT) index, and treatment completion was measured with the Dental Treatment Index (DTI). Oral health behaviours were also evaluated. Results: Significant differences between children and parents were observed only in the mild-to-moderate caries groups (DMFT I: 27 children vs. 12 parents; DMFT II: 15 children vs. 32 parents). No differences were found in the severe caries or caries-free groups. Children had lower treatment completion than parents in the poorest care group (DTI 1: 20 children vs. 7 parents), but similar outcomes in higher DTI categories. Among siblings, differences appeared only in the DMFT I group, with no differences in treatment completion or behaviours. Conclusions: Strong similarities in extreme dental characteristics between children and parents, comparable DTI values in most groups, and consistent sibling outcomes suggest that family environment strongly influences oral health. Full article

Current fractional flow reserve computed tomography (FFR_CT) methods use static imaging, potentially missing critical hemodynamic changes during the cardiac cycle. We developed a novel dynamic FFR_CT framework using 4D-CTA data to capture temporal coronary dynamics throughout the complete cardiac cycle. Our automated pipeline integrates 4D-CTA processing, temporally weighted geometric modeling, and patient-specific boundary conditions derived from actual flow measurements. Preliminary validation in three patients (four vessels) showed that dynamic FFRCT values (0.720, 0.797, 0.811, and 0.952) closely matched invasive FFR measurements (0.70, 0.78, 0.78, and 0.94) with improved accuracy compared to conventional static methods. The dynamic approach successfully captured physiologically relevant hemodynamic variations, addressing inter-patient variability limitations of standardized approaches. This study establishes the clinical feasibility of dynamic FFR_CT computation, potentially improving non-invasive coronary stenosis assessment for clinical decision-making and treatment planning. Full article

Currently, there are common reference intervals for creatinine levels in dogs, despite different weight categories. This can result in missing the diagnosis of kidney disease at an early stage in small dogs. The aim of this study was to determine and compare available markers of the GFR (serum cystatin C, creatinine and SDMA) in healthy small and miniature dogs. Each dog underwent the same protocol, including physical examination, blood pressure measurement and ultrasound of the urinary system. The serum filtration markers determined in the study were creatinine, cystatin C and SDMA. Urinalysis was performed, including urine specific gravity (USG) and the urine protein/creatinine ratio (UPC). Forty samples were included. The median values with the IQR (interquartile range) for creatinine, SDMA and cystatin C levels in the examined dogs were 63.8 µmol/L (16.7), 10.63 µg/dL (4.64) and 0.5 mg/L (0.2), respectively. Compared with Yorkshire Terriers and Chihuahuas, Maltese dogs had significantly higher median serum creatinine concentration (p < 0.001). Combining results of GFR markers, USG, UPC, patient’s clinical condition as well as the breed is mandatory when assessing renal function in small dogs. Maltese dogs may have physiologically higher serum creatinine levels. Full article

Smartphone-based strain monitoring of structural components is an emerging approach to structural health monitoring. However, the existing techniques suffer from limited accuracy and poor cross-device adaptability. This study aims to optimize the smartphone-based Micro Image Strain Sensing (MISS) method by replacing the traditional Connected Component Labeling (CCL) algorithm with the arc-support line segments (ASLS) algorithm, thereby significantly enhancing the stability and adaptability of circle detection in micro-images captured by diverse smartphones. Additionally, this study evaluates the impact of lighting conditions and lens distortion on the optimized MISS method. The experimental results demonstrate that the ASLS algorithm outperforms CCL in terms of recognition accuracy (maximum error of 0.94%) and cross-device adaptability, exhibiting greater robustness against color temperature and focal length variations. Under fluctuating lighting conditions, the strain measurement noise remains within ±0.5 με and with a maximum error of 7.0 με compared to LVDT measurements, indicating the strong adaptability of the optimized MISS method to external light changes. Barrel distortion in microscopic images induces a maximum pixel error of 5.66%, yet the final optimized MISS method achieves highly accurate strain measurements. The optimized MISS method significantly improves measurement stability and engineering applicability, enabling effective large-scale implementation for strain monitoring of civil infrastructure. Full article

Area shape function and frame compliance are the most critical parameters in nanoindentation, as they control measurement accuracy and represent the largest contributions to measurement uncertainty. Despite the availability of direct calibration methods, indirect calibrations are the most practical and fast. Thus, the indirect calibration methods proposed in ISO 14577-2 are most typically applied in academic and industrial research, as well as in quality controls. Previous research has highlighted some criticalities, but a holistic metrological framework was missing. This work aims to compare the performances of indirect calibration methods for area shape function and frame compliance in the nano-range, considering different alternatives suggested in the standard and most recent literature. The comparison will be based on uncertainty estimation using bootstrap estimation, which will innovatively highlight and introduce the effect of the nanoindentation dataset in the uncertainty estimation. The results show that the optimization of accuracy and uncertainty in mechanical characterization is achieved by indenting pairs of certified reference materials, resulting in a more robust approach to calibration experimental conditions than methods that require a single sample to be indented. Full article

In passive radar, the multiple model probability hypothesis density (MM-PHD) filter has demonstrated robust capability in tracking multi-maneuvering targets. Nevertheless, non-uniform clutter in practical scenarios causes misestimation of component weights, thereby generating false targets. To solve the false targets problem, a feature-matching MM-PHD (FM-MM-GM-PHD) algorithm for passive radar tracking is proposed in this paper. First, the measurement likelihood function was refined by leveraging target radar cross-section (RCS) and Doppler features to assist in suppressing false targets and reduce clutter interference. Additionally, the proposed algorithm incorporated adaptive component pruning and absorption processes to enhance tracking accuracy. Finally, a missed-alarm correction mechanism was introduced to compensate for measurement losses. Simulations of the passive radar results validated the findings that the proposed algorithm outperformed the traditional MM-PHD filter in both tracking accuracy and cardinality estimation. This superiority was particularly pronounced in non-uniform clutter environments under low detection probabilities. Full article

Crawling is an almost universal stage of locomotor development in infants; however, it is difficult to quantify using typical motion analysis techniques. The crawling stage therefore has underutilized potential to assess development and detect deviations or abnormalities. This study measured longitudinal weightbearing asymmetries in typically developing (TD) crawling children and compared this population to children with limb loss or limb differences (LLD) using a pressure-sensing mat. The LLD group bore significantly more weight using their arms vs. their legs than the TD group (p < 0.001), but even in cases of unilateral limb loss, bilateral weightbearing symmetry was similar to TD, controlling for body mass and age (p = 0.570). As children in the TD group developed and gained body mass, their weight shifted significantly to their left side (η² = 0.050) and away from their arms and toward their legs (η² = 0.255). The results provide insight into the biomechanical development of TD infant crawling, and the ways in which an atypically developing population manages weightbearing during crawling. The establishment of symmetry data will be useful, as crawling can serve as an opportunity for earlier detection of neuromotor conditions such as cerebral palsy. Furthermore, insight into the crawling patterns of children with limb loss and limb difference can inform prosthetic prescription and the need to consider a missing weight shift toward the legs as children develop. Full article

This paper presents a practical experiment for estimating the state-of-charge (SOC) of individual cells in a series-connected heterogeneous lithium-ion battery pack, where only the terminal voltage of the battery pack is measured. To deal with real-time computation constraints, the dense extended Kalman filter (DEKF) algorithm has been proposed in the literature, which has a significantly lower computational complexity compared to the regular extended Kalman filter for this specific estimation problem. This work supplements the existing work by conducting a real-world experiment to validate the performance of the DEKF. Specifically, experiments involving a battery pack of three cells connected in series were conducted, where the battery pack was discharged under a constant current load. A genetic algorithm was applied to identify missing model parameters, as well as tuning the DEKF for optimal convergence and accurate SOC estimation. Our experimental results confirm that the proposed DEKF accurately estimates the SOC of each cell regardless of the hardware limitations and uncertainty, making it suitable for low-cost, real-time battery management systems. In particular, the SOC estimation error can be kept well under 1% even if the initial estimate is far from the true SOC. Full article

This article presents a systematic literature review of data envelopment analysis (DEA) models used to evaluate circular economy (CE) practices. The review is based on 151 peer-reviewed articles published between 2015 and 2024. By analyzing this collection, this review categorizes different DEA models and their levels of application, discusses the data sources utilized, and identifies the prevailing methodologies and evaluation criteria used to measure the CE performance. Despite the extensive literature on measuring the circular economy using DEA, a critical evaluation of existing DEA approaches that highlights their strengths and weaknesses is still missing. Our analysis shows that DEA models provide valuable insights when assessing circular strategies, namely, R2—Reduce, R8—Recycling, and R9—Recovering. Over 40% of the surveyed literature focuses on China, with nearly 20% on the European Union. Other regions are sparsely represented within our sample, highlighting a potential gap in the current research landscape. Full article

Background: Life insurance markets are experiencing unprecedented transformation in the wake of economic disruption, evolving consumer expectations, and behavioral shifts following the COVID-19 pandemic. Traditional economic models often fail to capture the complex interplay of attitudinal, and cognitive factors that now shape insurance demand and premium selection. Methods: This study analyzes nationally representative survey data from over 3600 U.S. adults (2024 NAIC Financial Inclusion Survey). It uses a weighted full maximum likelihood Heckman selection model to identify determinants of life insurance uptake and premiums. The main innovation is modeling psychological price, a composite of perceived affordability, with higher-order polynomials. The design integrates psychometrically validated measures of financial knowledge and risk tolerance. Political ideology, race and ethnicity, and sources of financial advice serve as exclusion restrictions in the selection equation. Results: Psychological price shows an inverse-U relation with term outcomes: uptake rises at low to moderate affordability and declines at high affordability; among purchasers, term premiums rise at low to mid affordability and decline at high levels. For cash value policies, premiums decrease as psychological price increases. Financial knowledge and risk tolerance increase term uptake; financial knowledge reduces cash premiums. Education and income increase term uptake and term premiums. Compared with respondents reporting no ideology, conservative and centrist respondents have lower term uptake and higher cash uptake; using a professional advisor is associated with higher cash uptake. The selection correlation is positive for term ($\rho \approx 0.98$) and negative for cash ($\rho \approx -0.38$), indicating non-random selection in both markets. Implications: In order to reduce disparities, insurers should target the mid-affordability threshold with term offerings, streamline options for high-affordability consumers, offer pricing support and guidance for low-affordability households, increase uptake through advice channels and financial education, and address affordability barriers. Conclusions: Nonlinear affordability effects shape both market entry and pricing choices. Modeling psychological price with higher-order polynomials identifies thresholds and turning points that linear specifications miss. The results support targeted product design and outreach when perceived affordability drives insurance participation and premium choices. Full article

Background/Objectives: Family is a key protective factor for firefighters’ mental health, yet the home-related challenges of firefighting’s occupational demands remain underexplored in South Korea. This study aimed to establish an initial understanding of these challenges by conducting in-depth interviews with male firefighters and analyzing their lived experiences using a phenomenological approach. Methods: Twenty-nine married male firefighters (mean age = 43.4 years, range = 31–55) affiliated with the Busan Fire Department participated in individual telephone interviews between April and July 2022 during the COVID-19 pandemic. Data were analyzed using Giorgi’s descriptive phenomenological method, with NVivo Pro 12.0 employed for coding and organization. Results: Six themes were identified: (1) acting as an emergency commander at home, (2) reinterpreting traumatic experiences after marriage, (3) physical and emotional exhaustion from irregular schedules, (4) being national heroes misunderstood by families, (5) guilt-ridden and indebted Superman, and (6) striving to be Superman at home as well. These themes reflected the overarching meaning of a “lonely breadwinner struggling to be Superman both at work and home.” Participants described hyperarousal, emotional withdrawal, strained relationships, guilt over missed family events, and compensatory overextension. Distress was often concealed to protect families, but this limited emotional support. Conclusions: Korean male firefighters face significant work–family conflict shaped by cultural and occupational factors. These findings highlight the need for family-centered counseling, psychoeducation, and organizational interventions. Policy measures such as guaranteed rest after shifts, couple-based communication programs, and resilience-building initiatives are recommended to strengthen families as vital sources of psychological resilience. Full article

CblC deficiency is the most common intracellular disorder of vitamin B12 metabolism. Expanded newborn screening (NBS) plays a key role in early diagnosis, allowing timely treatment and preventing serious complications. However, traditional first-tier markers—such as propionylcarnitine (C3) and its ratios with other metabolites (e.g., methionine, carnitine, and acetylcarnitine)—have limited sensitivity, particularly for mild forms, leading to missed or delayed diagnoses. In this study, we analyzed data from the NBS Center of the Lazio region (Italy) and identified nine newborns with confirmed CblC deficiency. All were recalled due to abnormalities in C3 or related ratios, along with elevated methylmalonic acid (MMA) levels. Notably, three infants had completely normal C3 levels and ratios during the second screening test, yet they showed MMA levels above the cut-off value (2 µmol/L), enabling a diagnosis of otherwise undetectable mild CblC cases. Our center regularly measures MMA in dried blood spots, even when first-tier markers return to normal on the second sample. This approach allows for early diagnosis and immediate treatment with hydroxocobalamin in patients with mild CblC deficiency, resulting in early intervention, effective metabolic control, and, based on current follow-up, normal neurodevelopmental outcomes. Our findings highlight the essential role of second-tier MMA testing in improving the detection of mild CblC deficiency during NBS. Full article