Search Results (154)

This study presents a critical evaluation of image-based 3D reconstruction techniques for small archaeological artifacts, focusing on a quantitative comparison between Neural Radiance Fields (NeRF), its recent Gaussian Splatting (GS) variant, and traditional Structure-from-Motion (SfM) photogrammetry. The research targets artifacts smaller than 5 cm, characterized by complex geometries and reflective surfaces that pose challenges for conventional recording methods. To address the limitations of traditional methods without resorting to the high costs associated with laser scanning, this study explores NeRF and GS as cost-effective and efficient alternatives. A comprehensive experimental framework was established, incorporating ground-truth data obtained using a metrological articulated arm and a rigorous quantitative evaluation based on root mean square (RMS) error, Chamfer distance, and point cloud density. The results indicate that while NeRF outperforms GS in terms of geometric fidelity, both techniques still exhibit lower accuracy compared to SfM, particularly in preserving fine geometric details. Nonetheless, NeRF demonstrates strong potential for rapid, high-quality 3D documentation suitable for visualization and dissemination purposes in cultural heritage. These findings highlight both the current capabilities and limitations of neural rendering techniques for archaeological documentation and suggest promising future research directions combining AI-based models with traditional photogrammetric pipelines. Full article

Numerous rural residential buildings exhibit inadequate seismic performance when subjected to blast-induced vibrations, which poses potential threats to their overall stability and structural integrity when in proximity to blasting project sites. The investigation conducted in conjunction with the Qianshi Mountain blasting operations along the Wenzhou segment of the Hangzhou–Wenzhou High-Speed Railway integrates household field surveys and empirical measurements to perform modal analysis of rural residential buildings through finite element simulation. Adhering to the principle of stratified arrangement and composite measurement point configuration, an effective and reasonable experimental observation framework was established. In this investigation, the seven-story rural residential building in adjacent villages was selected as the research object. Strong-motion seismographs were strategically positioned adjacent to frame columns on critical stories (ground, fourth, seventh, and top floors) within the observational system to acquire test data. Methodical signal processing techniques, including effective signal extraction, baseline correction, and schedule conversion, were employed to derive temporal dynamic characteristics for each story. Combined with the Fourier transform, the frequency–domain distribution patterns of different floors are subsequently obtained. Leveraging the structural dynamic theory, time–domain records were mathematically converted to establish the structure’s maximum response spectra under blast-induced loading conditions. Through the analysis of characteristic curves, including floor acceleration response spectra, dynamic amplification coefficients, and spectral ratios, the dynamic response patterns of rural residential buildings subjected to blast-induced vibrations have been elucidated. Following the normalization of peak acceleration and velocity parameters, the mechanisms underlying differential floor-specific dynamic responses were examined, and the layout principles of measurement points were subsequently formulated and summarized. These findings offer valuable insights for enhancing the seismic resilience and structural safety of rural residential buildings exposed to blast-induced vibrations, with implications for both theoretical advancements and practical engineering applications. Full article

The advent of mixed reality (MR) systems has revolutionized human–computer interactions by seamlessly integrating virtual elements with the real world. Devices like the HoloLens 2 (HL2) enable intuitive, hands-free interactions through advanced hand-tracking technology, making them valuable in fields such as education, healthcare, engineering, and training simulations. However, despite the growing adoption of MR, there is a noticeable lack of comprehensive comparisons between the hand-tracking accuracy of the HL2 and high-precision benchmarks like motion capture systems. Such evaluations are essential to assess the reliability of MR interactions, identify potential tracking limitations, and improve the overall precision of hand-based input in immersive applications. This study aims to assess the accuracy of HL2 in tracking hand position and measuring kinematic hand parameters, including joint angles and lateral pinch span (distance between thumb and index fingertips), using its tracking data. To achieve this, the Vicon motion capture system (VM) was used as a gold-standard reference. Three tasks were designed: (1) finger tracing of a 2D pattern in 3D space, (2) grasping various common objects, and (3) lateral pinching of objects with varying sizes. Task 1 tests fingertip tracking, Task 2 evaluates joint angle accuracy, and Task 3 examines the accuracy of pinch span measurement. In all tasks, HL2 and VM simultaneously recorded hand positions and movements. The data captured in Task 1 were analyzed to evaluate HL2’s hand-tracking capabilities against VM. Finger rotation angles from Task 2 and lateral pinch span from Task 3 were then used to assess HL2’s accuracy compared to VM. The results indicate that the HL2 exhibits millimeter-level errors compared to Vicon’s tracking system in Task 1, spanning in a range from 2 mm to 4 mm, suggesting that HL2’s hand-tracking system demonstrates good accuracy. Additionally, the reconstructed grasping positions in Task 2 from both systems show a strong correlation and an average error of 5°, while in Task 3, the accuracy of the HL2 is comparable to that of VM, improving performance as the object thickness increases. Full article

This paper presents the results of statistical analyses carried out for the input energy velocity (equivalent velocity to be used for the determination of the input energy) of equivalent single-degree-of-freedom systems with definite strength. An earthquake ground motion database, which includes 268 far-field records and two horizontal components from 134 recording stations located on firm sites, is employed for nonlinear time–history analysis. The probabilistic distribution of the input energy velocity is investigated for the candidate distribution models through a chi-square test, and the lognormal distribution was found as the most representative distribution model. Furthermore, the data used for analysis are classified with respect to the considered strength reduction factors of SDOF systems as a structural parameter and the effective duration of the considered strong ground motions as a ground motion parameter. The effect of those parameters on input energy velocity is investigated by using probabilistic techniques such as t-tests and ANOVAs. It is concluded that the strength reduction factor influences the input energy velocity along the particular period ranges of SDOF systems. Furthermore, the effective duration of the ground motion is another effective parameter on input energy velocity for almost all the considered period ranges. An equation is proposed for the determination of input energy velocity in terms of the aforementioned parameters. Full article

As transportation infrastructure systems continue to expand, the demand for unmanned aerial vehicle (UAV) technologies in the assessment of urban infrastructure is expected to grow substantially, due to their strong potential for efficient data collection and post-processing. UAVs offer numerous advantages in infrastructure assessment, including enhanced time and cost efficiency, improved safety, and the ability to capture high-quality data. Furthermore, integrating various data-collecting sensors enhances the versatility of UAVs, enabling the acquisition of diverse data types to support comprehensive infrastructure evaluations. Numerous post-processing software applications utilizing various structure-from-motion (SfM) techniques have been developed, significantly facilitating the assessment process. However, researchers’ efforts to find the potentialities of this technology will be in vain if its applications are not utilized effectively in the practical field. Therefore, this study aims to determine the adaptation condition of UAV technologies in different Department of Transportation (DOT) and Federal Highway Administration (FHWA) agencies to assess transportation infrastructure systems. This study also explores the quantitative analysis of benefits and challenges/barriers, expectations for every UAV and post-processing software, and the cutting-edge features that should be integrated with UAVs to effectively evaluate transportation infrastructure systems. A comprehensive survey form was distributed to all 50 DOTs and the FHWA, and 35 complete responses were recorded from 27 DOTs and the FHWA. The survey results show that 25 agencies currently use UAVs for roads or highways, and 23 DOTs for bridges, confirming these as the most commonly assessed infrastructure systems. The top benefits found in this study include safety, cost effectiveness, and time efficiency (mean ratings: 3.95–4.28), while weather, FAA regulations, and airspace restrictions are the main challenges. Respondents emphasize the need for longer flight times, better automation, and advanced data tools, underscoring growing adoption and highlighting the need to overcome technical, regulatory, and data privacy challenges for optimal UAV integration within transportation infrastructure systems management. Full article

Inertial measurement units (IMUs) have gained popularity as portable and cost-effective alternatives to optoelectronic motion capture systems for assessing joint kinematics. This study aimed to validate a commercially available multi-sensor IMU-based system against a laboratory-grade motion capture system across lower limb, trunk, and upper limb movements. Fifteen healthy participants performed a battery of single- and multi-joint tasks while motion data were simultaneously recorded by both systems. Range of motion (ROM) values were extracted from the two systems and compared. The IMU-based system demonstrated high concurrent validity, with non-significant differences in most tasks, root mean square error values generally below 7°, percentage of similarity greater than 97%, and strong correlations (r ≥ 0.77) with the reference system. Systematic biases were trivial (≤3.9°), and limits of agreement remained within clinically acceptable thresholds. The findings indicate that the tested IMU-based system provides ROM estimates statistically and clinically comparable to those obtained with optical reference systems. Given its portability, ease of use, and affordability, the IMU-based system presents a promising solution for motion analysis in both clinical and remote rehabilitation contexts, although future research should extend validation to pathological populations and longer monitoring periods. Full article

The detection of weak moving targets in heterogeneous clutter backgrounds is a significant challenge in radar systems. In this paper, we propose a track-before-detect (TBD) method based on information geometry (IG) theory applied to range-azimuth measurements, which extends the IG detectors to multi-frame detection through inter-frame information integration. The approach capitalizes on the distinctive benefits of the information geometry detection framework in scenarios with strong clutter, while enhancing the integration of information across multiple frames within the TBD approach. Specifically, target and clutter trajectories in multi-frame range-azimuth measurements are modeled on the Hermitian positive definite (HPD) and power spectrum (PS) manifolds. A scoring function based on information geometry, which uses Kullback–Leibler (KL) divergence as a geometric metric, is then devised to assess these motion trajectories. Moreover, this study devises a solution framework employing dynamic programming (DP) with constraints on state transitions, culminating in an integrated merit function. This algorithm identifies target trajectories by maximizing the integrated merit function. Experimental validation using real-recorded sea clutter datasets showcases the effectiveness of the proposed algorithm, yielding a minimum 3 dB enhancement in signal-to-clutter ratio (SCR) compared to traditional approaches. Full article

Vertical PGA is frequently included in civil engineering regulations simply by multiplying the horizontal PGA by a constant. Moreover, most design codes, including Eurocode 8, do not consider the impact of the local soil on vertical ground motion at all. In this study, we demonstrate that such practices increase earthquake risks. The article examines vertical PGA strong-motion estimations for the city of Banja Luka. Banja Luka serves as a case study for areas with records of moderate to strong earthquakes and diverse deep geological conditions. Regional equations for scaling vertical PGA are presented. The vertical PGA values and vertical to horizontal PGA ratios are calculated and analyzed. The findings indicate that the vertical to horizontal PGA ratios for the rock sites depend on the source-to-site distance and deep geology and fall between 0.30 and 0.66. Hence, these ratios cannot be approximated by a single value of 0.90 and 0.45, as specified by Eurocode 8 for Type 1 and Type 2 spectra, respectively. Moreover, the results show that the deep geology effects on vertical ground motion can exceed the local soil effects. When the amount of recorded data from comparable areas increases, we will be able to properly calibrate the existing scaling equations and obtain more reliable estimates of vertical PGA. Full article

Motivated by two of the most unexpected discoveries in recent years—the detection of ArH⁺ and ${\mathrm{HeH}}^{+}$ noble gas molecules in the cold, low-pressure regions of the Universe—we investigate [He₂H]⁺ and [Ne₂H]⁺ as potentially detectable species in the interstellar medium, providing new insights into their energetic and spectral properties. These findings are crucial for advancing our understanding of noble gas chemistry in astrophysical environments. To achieve this, we employed a data-driven approach to construct a high-accuracy machine-learning potential energy surface using the reproducing kernel Hilbert space method. Training and testing datasets are generated via high-level CCSD(T)/CBS[56] quantum chemistry computations, followed by a rigorous validation protocol to ensure the reliability of the potential. The ML-PES is then used to compute vibrational states within the MCTDH framework, and assign spectral transitions for the most common isotopologues of these species in the interstellar medium. Our results are compared with previously recorded values, revealing that both cations exhibit a prominent proton-shuttle motion within the infrared spectral range, making them strong candidates for telescopic observation. This study provides a solid computational foundation, based on rigorous, fully quantum treatments, aiming to assist in the identification of these yet unobserved He/Ne hydride cations in astrophysical environments. Full article

This study focuses on determining the high-frequency decay parameter kappa (k) and its site component (k₀) for sixteen accelerometric stations installed in suitable locations in North Macedonia. Kappa characterizes the attenuation of ground motion at high frequencies, describing the decrease in the acceleration amplitude spectrum. It is defined using a regression line in log-linear space, starting from the point where the S-wave amplitude spectrum begins to decay rapidly. The site characteristics of the stations are determined through geophysical and borehole investigations, as well as HVSR mean curves derived from earthquake data. The strong-motion data used in this analysis originate from earthquake events with a moment magnitude greater than 3 (M_W > 3), an epicentral distance less than 120 km (R_epi < 120 km), and a focal depth lower than 30 km (h < 30 km). The records undergo visual inspection and filtering, with those having a signal-to-noise ratio (SNR) below 3 excluded from further analysis. The study examines the correlation between kappa values and various parameters, including magnitude, epicentral distance, average shear-wave velocity in the top 30 m depth (V_S30), and fundamental site frequency (f₀). The importance of this study is the application in the future evaluation/update of seismic hazard analysis of the region. Full article

Wearable inertial measurement units (IMUs) are increasingly used in human motion analysis due to their ability to measure movement in real-world environments. However, with rapid technological advancement and a wide variety of models available, it is essential to evaluate their performance and suitability for analyzing specific body regions. This study aimed to assess the accuracy and precision of an IMU-based sensor in measuring trunk range of motion (ROM). Twenty-seven healthy adults (11 males, 16 females; mean age: 31.1 ± 11.0 years) participated. Each performed trunk movements—flexion, extension, lateral bending, and rotation—while angular data were recorded simultaneously using a single IMU and a marker-based optoelectronic motion capture (MoCap) system. Analyses included accuracy indices, Root Mean Square Error (RMSE), Pearson’s correlation coefficient (r), concordance correlation coefficient (CCC), and Bland–Altman limits of agreement. The IMU showed high accuracy in rotation (92.4%), with strong correlation (r = 0.944, p < 0.001) and excellent agreement [CCC = 0.927; (0.977–0.957)]. Flexion (72.1%), extension (64.1%), and lateral bending (61.4%) showed moderate accuracy and correlations (r = 0.703, 0.564, and 0.430, p < 0.05). The RMSE ranged from 1.09° (rotation) to 3.01° (flexion). While the IMU consistently underestimated ROM, its accuracy in rotation highlights its potential as a cost-effective MoCap alternative, warranting further study for broader clinical use. Full article

Predicting macroseismic intensity from instrumental ground motion parameters remains a complex task due to the nonlinear relationship with observed damage patterns. An explainable machine learning model based on the XGBoost algorithm was developed to address the challenge. The model is trained on data from Italian earthquakes recorded between 1972 and 2016, linking ground motion recordings to MCS observations located within 3 km. The dataset has been enhanced with site-specific correction factors to better capture local amplification effects. Key input features include Arias Intensity, spectral accelerations at four representative periods (0.15 s, 0.4 s, 0.6 s, and 2 s), and site condition proxies, such as slope and Vs30. The model achieves strong predictive performance (RMSE = 0.73, R² = 0.76), corresponding to a 33% reduction in residual standard deviation compared to traditional GMICE-based regression methods. To ensure transparency, Shapley Additive Explanations (SHAPs) are used to quantify the contribution of each feature. Arias Intensity emerges as the dominant predictor, followed by spectral ordinates in line with structural response mechanics. As damage severity increases, feature importance shifts from PGA to PGV, while site-specific variables (slope, Vs30) act as refiners rather than amplifiers of shaking. The proposed approach enables near real-time prediction of local damage scenarios and supports data-driven decision-making in seismic emergency management. Full article

Background: Degenerative lumbar disease (DLD) affects older adults, causing lumbar degeneration and lower extremity dysfunction. The five-times sit-to-stand test (5xSTS) reveals kinematic changes associated with DLD. While marker-based motion capture systems detect these changes, their complexity limits clinical use. Markerless motion capture offers a portable alternative, yet its functional assessment applications in DLD remain underexplored. Thus, the aim of this study is to evaluate the reliability and validity of markerless motion capture for assessing functional tests in DLD patients. Methods: This study included 11 healthy individuals (mean age: 27.28 ± 6.92 years) and 10 with DLD (mean age: 70.00 ± 8.08 years). Participants performed the 5xSTS while being recorded by marker-based (VICON) and markerless (MediaPipe) systems using two synchronized cameras. Test–retest reliability was assessed over one week via the intraclass correlation coefficient (ICC). Concurrent validity and agreement between VICON and MediaPipe were evaluated via Pearson/Spearman correlation coefficients, systematic bias, and the root mean square error (RMSE). Movement time, joint excursions, and angular velocities were also analyzed and compared across two groups. Results: Both systems showed high test–retest reliability (ICC: 0.81–0.99) and strong correlations (r: 0.75–0.99). The highest RMSE was observed at the ankle in the anterior–posterior (A–P) direction in the DLD group (54.55 mm) and at the hip A–P axis in the control group (51.20 mm). The lowest RMSE was found at the knee medial–lateral (M–L) axis in the DLD group (7.88 mm) and at the ankle M–L axis in the control group (8.54 mm). Bias values ranged from 0.30 mm (hip vertical in control group) to +53.47 mm (ankle A–P in DLD group), with underestimation more common at the hip and overestimation at the ankle. The control group demonstrated a faster 5xSTS completion time (5.89 ± 0.69 s vs. 8.13 ± 1.96 s, p < 0.05), greater hip joint excursions during sit-to-stand (65.07 ± 25.94° vs. 38.13 ± 9.84°, p < 0.05) and stand-to-sit (62.56 ± 24.74° vs. 27.85 ± 11.45°, p < 0.05) tests, and higher angular velocities compared to the DLD group. Conclusion: MediaPipe markerless motion capture can quantify 3D kinematic changes in DLD patients during functional performance. It enables a clinical evaluation with minimal setup, offers potential for remote assessment, and accurately detects sagittal plane movement. The two-camera system provides 3D kinematic data comparable to multi-camera systems, suitable for home rehabilitation and assessment. Full article

This study evaluates the performance of a 32-marker motion capture (MoCap) system in estimating respiratory frequency ($f_R$) and tidal volume ($V_T$) during cycling exercise. Fourteen well-trained cyclists performed an incremental step test on a cycle ergometer, while simultaneously recording a raw flow signal with a reference metabolic cart (COSMED) and respiratory-induced torso movements with twelve optoelectronic cameras registering the position of 32 markers affixed to the torso. $f_R$ and $V_T$ were calculated from both systems on a breath-by-breath basis. The MoCap system showed a strong correlation with the COSMED system when measuring $f_R$ and $V_T$ ($r^2$ = 0.99, $r^2$ = 0.87, respectively) during exercise. For $f_R$, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 0.79 breaths/min and 2.1%, respectively. For $V_T$, MoCap consistently underestimated values compared to COSMED, showing a bias (MOD ± LOA) of −0.11 ± 0.42 L and MAPE values of 8%. These findings highlight the system’s capabilities for real-time respiratory monitoring in athletic environments. Full article

(1) Background: Marker-based optical motion tracking is the gold standard in gait analysis; however, markerless solutions are rapidly emerging today. Algorithms like Openpose can track human movement from a video. Few studies have assessed the validity of this method. This study aimed to assess the reliability of Openpose in measuring the kinematics and spatiotemporal gait parameters. (2) Methods: This analysis used simultaneously recorded video and optoelectronic motion capture data. We assessed 20 subjects with different gait impairments (healthy, right hemiplegia, left hemiplegia, paraparesis). The two methods were compared using computing absolute errors (AEs), intraclass correlation coefficients (ICCs), and cross-correlation coefficients (CCs) for normalized gait cycle joint angles. (3) Results: The spatiotemporal parameters showed an ICC between good to excellent, and the absolute error was very small: cadence AE = 1.63 step/min, Mean Velocity AE = 0.16 m/s. The Range of Motion (ROM) showed a good to excellent agreement in the sagittal plane. Furthermore, the normalized gait cycle CCC values indicated moderate to strong coupling in the sagittal plane. (4) Conclusions: We found Openpose to be accurate for sagittal plane gait kinematics and for spatiotemporal gait parameters in the healthy and pathological subjects assessed. Full article