Search Results (2,234)

A cross-sectional study was conducted to biomechanically characterize Isabel’s workout (30 snatch repetitions with 61 kg fixed weight), focusing on eventual changes in knee, hip and shoulder angles. A three-dimensional markerless motion capture system was used to collect data from 11 highly trained male crossfitters along the Isabel workout performed at maximal effort. The routine was analyzed globally and in initial, middle and final phases (10, 20 and 30 repetitions, respectively). Lift total time increased (1.51 ± 0.18 vs. 1.97 ± 0.20 s) and maximal lift velocity (2.64 ± 0.12 vs. 2.32 ± 0.13 m/s) and maximal lift power (15.58 ± 2.34 vs. 13.80 ± 2.49 W/kg) decreased from the initial to final phases, while the time from lift until the bar crossed the hip and shoulder (34.20 ± 4.00 vs. 27.50 ± 5.10 and 39.70 ± 16.80 vs. 30.90 ± 13.90%) decreased along the Isabel workout. In addition, a decrease in hip flexion was observed during the last two phases when the bar crosses the knee (62.62 ± 24.80 vs. 53.60 ± 19.99°). Data evidence a decrease in the power profile and a change in hip flexion throughout the Isabel workout, without compromising the other joints. Full article

The accurate estimation of lower-limb joint angles and moments is crucial for assessing the progression of orthopedic diseases, with continuous monitoring during daily walking being essential. An inertial measurement unit (IMU) attached to the lower back has been used for this purpose, but the effect of IMU misalignment in the frontal plane on estimation accuracy remains unclear. This study investigated the impact of virtual IMU misalignment in the frontal plane on estimation errors of lower-limb joint angles and moments during walking. Motion capture data were recorded from 278 healthy adults walking at a comfortable speed. An estimation model was developed using principal component analysis and linear regression, with pelvic accelerations as independent variables and lower-limb joint angles and moments as dependent variables. Virtual IMU misalignments of −20°, −10°, 0°, 10°, and 20° in the frontal plane (five conditions) were simulated. The joint angles and moments were estimated and compared across these conditions. The results indicated that increasing virtual IMU misalignment in the frontal plane led to greater errors in the estimation of pelvis and hip angles, particularly in the frontal plane. For misalignments of ±20°, the errors in pelvis and hip angles were significantly amplified compared to well-aligned conditions. These findings underscore the importance of accounting for IMU misalignment when estimating these variables. Full article

This paper proposes a composite model that combines convolutional neural network models and mechanical analysis to determine the forces acting on an object. First, we establish a model using Newtonian mechanics to analyze the forces experienced by the human body during movement, particularly the forces on joints. The model calculates the mapping relationship between the object’s movement and the forces on the joints. Then, by analyzing a large number of fencing competition videos using a deep learning model, we extract video features to study the torques and forces on human joints. Our analysis of numerous images reveals that, in certain movement patterns, the peak pressure on the knee joint can be two to three times higher than in a normal state, while the driving knee can withstand peak torques of 400–600 Nm. This straightforward model can effectively capture the forces and torques on the human body during movement using a deep neural network. Furthermore, this model can also be applied to problems involving non-rigid body motion. Full article

Everyone aspires to live a healthy life, but many will inevitably experience some form of disease, illness, or accident that results in disability at some point. Rehabilitation plays a crucial role in helping individuals recover from these disabilities and return to their daily activities. Traditional rehabilitation methods are often expensive, are inefficient, and lead to slow progress for patients. However, in this era of technology, various sensor-based automatic rehabilitation is also possible. A Kinect sensor is a skeletal tracking device that captures human motions and gestures. It can provide feedback to the users, allowing them to better understand their progress and adjust their movements accordingly. In this study, stroke-based rehabilitation is presented along with the Toronto Rehab Stroke Pose Dataset (TRSP). Pre-processing of the raw dataset was performed using various features, and several state-of-the-art classifiers were applied to evaluate the data provided by the Kinect sensor. Among the various classifiers, eXtreme Gradient Boosing (XGB) attained the maximum accuracy of 92% for the TRSP dataset. Furthermore, hyperparameters of the XGB have been optimized using a metaheuristic gray wolf optimizer for better performance. Full article

The Athens Stock Exchange (ASE) is a dynamic financial market with complex interactions and inherent volatility. Traditional models often fall short in capturing the intricate dependencies and long memory effects observed in real-world financial data. In this study, we explore the application of fractional Brownian motion (fBm) to model stock price dynamics within the ASE, specifically utilizing the Athens General Composite (ATG) index. The ATG is considered a key barometer of the overall health of the Greek stock market. Investors and analysts monitor the index to gauge investor sentiment, economic trends, and potential investment opportunities in Greek companies. We find that the Hurst exponent falls outside the range typically associated with fractal Brownian motion. This, combined with the established non-normality of increments, disfavors both geometric Brownian motion and fractal Brownian motion models for the ATG index. Full article

This study aimed to map and analyze the spatial pattern of the surface deformation associated with the 2024 Noto Peninsula earthquake (Mw7.5) using structure-from-motion/multi-view-stereo (SfM–MVS), an advanced photogrammetric technique. The analysis was conducted using digital aerial photographs with a ground pixel dimension of 0.2 m (captured the day after the earthquake). Horizontal locations of GCPs were determined using pre-earthquake data to remove the wide-area horizontal crustal deformation component. The elevations of the GCPs were corrected by incorporating quasi-vertical values derived from a 2.5-dimensional analysis of synthetic aperture radar (SAR) results. In the synclinorium structure area, where no active fault had previously been identified, we observed a 5 km long uplift zone (0.1 to 0.2 km in width), along with multiple scarps that reached a maximum height of 2.2 m. The area and shape of the surface deformation suggested that the induced uplift and surrounding landslides were related to fold structures and their growth. Thus, our study shows the efficacy of SfM–MVS with respect to accurately mapping earthquake-induced deformations, providing crucial data for understanding seismic activity and informing disaster-response strategies. Full article

Tremor, defined as an “involuntary, rhythmic, oscillatory movement of a body part”, is a key feature of many neurological conditions including Parkinson’s disease and essential tremor. Clinical assessment continues to be performed by visual observation with quantification on clinical scales. Methodologies for objectively quantifying tremor are promising but remain non-standardized across centers. Our center performs full-body behavioral testing with 3D motion capture for clinical and research purposes in patients with Parkinson’s disease, essential tremor, and other conditions. The objective of this study was to assess the ability of several candidate processing pipelines to identify the presence or absence of tremor in kinematic data from patients with confirmed movement disorders and compare them to expert ratings from movement disorders specialists. We curated a database of 2272 separate kinematic data recordings from our center, each of which was contemporaneously annotated as tremor present or absent by a movement physician. We compared the ability of six separate processing pipelines to recreate clinician ratings based on F1 score, in addition to accuracy, precision, and recall. The performance across algorithms was generally comparable. The average F1 score was 0.84±0.02 (mean ± SD; range 0.81–0.87). The second highest performing algorithm (cross-validated F1=0.87) was a hybrid that used engineered features adapted from an algorithm in longstanding clinical use with a modern Support Vector Machine classifier. Taken together, our results suggest the potential to update legacy clinical decision support systems to incorporate modern machine learning classifiers to create better-performing tools. Full article

Accurate seismic ground response analysis is crucial for the design and safety of civil infrastructure and establishing effective mitigation measures against seismic risks and hazards. This is a complex process due to the nonlinear soil properties and complicated underground geometries. As a simplified approach, the one-dimensional wave propagation model, which assumes that seismic waves travel vertically through a horizontally layered medium, is widely adopted for its reasonable performance in many practical applications. This study explores the potential of sequence deep learning models, specifically 1D convolutional neural networks (1D-CNNs), long short-term memory (LSTM) networks, and transformers, as an alternative for seismic ground response modeling. Utilizing ground motion data from the Kiban Kyoshin Network (KiK-net), we train these models to predict ground surface acceleration response spectra based on bedrock motions. The performance of the data-driven models is compared with the conventional equivalent-linear analysis model, SHAKE2000. The results demonstrate that the deep learning models outperform the physics-based model across various sites, with the transformer model exhibiting the smallest average prediction error due to its ability to capture long-range dependencies. The 1D-CNN model also shows a promising performance, albeit with occasional higher errors than the other models. All the data-driven models exhibit efficient computation times of less than 0.4 s for estimation. These findings highlight the potential of sequence deep learning approaches for seismic ground response modeling. Full article

The dispersion of elastic shear waves in multilayered bodies is a topic of extensive research due to its significance in contemporary science and engineering. Anti-plane shear motion, a two-dimensional mathematical model in solid mechanics, effectively captures shear wave propagation in elastic bodies with relative mathematical simplicity. This study models the vibration of elastic waves in a multilayered inhomogeneous circular membrane using the Helmholtz equation with fractional-order infusion, effectively leveraging the anti-plane shear motion equation to avoid the computational complexity of universal plane motion equations. The method of the separation of variables and the conformable Bessel equation are utilized for the analytical examination of the model’s resulting vibrational displacements, as well as the dispersion relation. Additionally, the influence of various wave phenomena, including the dependencies of the wavenumber on the frequency and the phase speed on the wavenumber, respectively, with the variational effect of the fractional order on wave dispersion is considered. Numerical simulations of prototypical cases validate the formulated model, illustrating its applicability and effectiveness. The study reveals that fractional-order infusion significantly impacts the dispersion of elastic waves in both single- and multilayer membranes. The effects vary depending on the membrane’s structure and the wave propagation regime (long-wave vs. short-wave). These findings underscore the potential of fractional-order parameters in tailoring wave behavior for diverse scientific and engineering applications. Full article

Rockfalls are natural geological phenomena characterized by the abrupt detachment and freefall descent of rock fragments from steep slopes. These events exhibit considerable variability in scale, velocity, and trajectory, influenced by the geological composition of the slope, the topography, and other environmental conditions. By employing advanced modeling techniques and terrain analysis, researchers aim to predict and control rockfall hazards to prevent casualties and protect properties in areas at risk. In this study, two rockfall events in the villages of Myloi and Platiana of Ilia prefecture were examined. The research was conducted by means of HY-STONE software, which performs 3D numerical modeling of the motion of non-interacting blocks. To perform this modeling, input files require the processing of base maps and datasets in a GIS environment. Stochastic modeling and 3D descriptions of slope topography, based on Digital Elevation Models (DEMs) without spatial resolution limitations, ensure multiscale analysis capabilities. Considering this capability, seven freely available DEMs, derived from various sources, were applied in HY-STONE with the scope of performing a large number of multiparametric analyses and selecting the most appropriate and efficient DEM for the software requirements. All the necessary data for the multiparametric analyses were generated within a GIS environment, utilizing either the same restitution coefficients and rolling friction coefficient or varying ones. The results indicate that finer-resolution DEMs capture detailed terrain features, enabling the precise identification of rockfall source areas and an accurate depiction of the kinetic energy distribution. Further, the results show that a correct application of the model to different DEMs requires a specific parametrization to account for the different roughness of the models. Full article

The drift-diffusion model (DDM) is a common approach to understanding human decision making. It considers decision making as accumulation of evidence about visual stimuli until sufficient evidence is reached to make a decision (decision boundary). Recently, Smith and colleagues proposed an extension of DDM, the time-varying DDM (TV-DDM). Here, the standard simplification that evidence accumulation operates on a fully formed representation of perceptual information is replaced with a perceptual integration stage modulating evidence accumulation. They suggested that this model particularly captures decision making regarding stimuli with dynamic noise. We tested this new model in two studies by using Bayesian parameter estimation and model comparison with marginal likelihoods. The first study replicated Smith and colleagues’ findings by utilizing the classical random-dot kinomatogram (RDK) task, which requires judging the motion direction of randomly moving dots (motion discrimination task). In the second study, we used a novel type of stimulus designed to be like RDKs but with randomized hue of stationary dots (color discrimination task). This study also found TV-DDM to be superior, suggesting that perceptual integration is also relevant for static noise possibly where integration over space is required. We also found support for within-trial changes in decision boundaries (“collapsing boundaries”). Interestingly, and in contrast to most studies, the boundaries increased with increasing task difficulty (amount of noise). Future studies will need to test this finding in a formal model. Full article

Drone aerial videos have immense potential in surveillance, rescue, agriculture, and urban planning. However, accurately tracking multiple objects in drone aerial videos faces challenges like occlusion, scale variations, and rapid motion. Current joint detection and tracking methods often compromise accuracy. We propose a drone multiple object tracking algorithm based on a holistic transformer and multiple feature trajectory matching pattern to overcome these challenges. The holistic transformer captures local and global interaction information, providing precise detection and appearance features for tracking. The tracker includes three components: preprocessing, trajectory prediction, and matching. Preprocessing categorizes detection boxes based on scores, with each category adopting specific matching rules. Trajectory prediction employs the visual Gaussian mixture probability hypothesis density method to integrate visual detection results to forecast object motion accurately. The multiple feature pattern introduces Gaussian, Appearance, and Optimal subpattern assignment distances for different detection box types (GAO trajectory matching pattern) in the data association process, enhancing tracking robustness. We perform comparative validations on the vision-meets-drone (VisDrone) and the unmanned aerial vehicle benchmarks; the object detection and tracking (UAVDT) datasets affirm the algorithm’s effectiveness: it obtained 38.8% and 61.7% MOTA, respectively. Its potential for seamless integration into practical engineering applications offers enhanced situational awareness and operational efficiency in drone-based missions. Full article

Skeleton-based action recognition, renowned for its computational efficiency and indifference to lighting variations, has become a focal point in the realm of motion analysis. However, most current methods typically only extract global skeleton features, overlooking the potential semantic relationships among various partial limb motions. For instance, the subtle differences between actions such as “brush teeth” and “brush hair” are mainly distinguished by specific elements. Although combining limb movements provides a more holistic representation of an action, relying solely on skeleton points proves inadequate for capturing these nuances. Therefore, integrating detailed linguistic descriptions into the learning process of skeleton features is essential. This motivates us to explore integrating fine-grained language descriptions into the learning process of skeleton features to capture more discriminative skeleton behavior representations. To this end, we introduce a new Linguistic-Driven Partial Semantic Relevance Learning framework (LPSR) in this work. While using state-of-the-art large language models to generate linguistic descriptions of local limb motions and further constrain the learning of local motions, we also aggregate global skeleton point representations and textual representations (which generated from an LLM) to obtain a more generalized cross-modal behavioral representation. On this basis, we propose a cyclic attentional interaction module to model the implicit correlations between partial limb motions. Numerous ablation experiments demonstrate the effectiveness of the method proposed in this paper, and our method also obtains state-of-the-art results. Full article

The ground motion in the near-fault region of an earthquake is characterized by exceptional energy levels, powerful velocity impulses, substantial spatial variability, and notable permanent displacement. These unique attributes can dramatically escalate structural damage. Steel truss arch bridges, being critical components of transportation networks, are particularly vulnerable to these phenomena due to their extensive stiffness spans. Such factors are difficult to accurately simulate. In this study, real near-fault ground motions that incorporate spatial variability effects and pulse effects are used to excite the long-span arch bridge, thereby striving to realistically reproduce the structural damage sustained by the bridge under the simultaneous influence of near-fault spatial variability and pulse effects. This study adopts an arch bridge with a span closely approximating the spacing between stations (200 m) of the SMART seismic array as a case study. The near-fault ground motions, characterized by spatial variability and captured by the array, are selected as seismic samples, while the far-field ground motions recorded by the same array serve as a comparative reference. The seismic excitations are then input into the bridge case study, following the spatial correspondence of the stations, using a large-scale finite element program to obtain the structural response. Upon analyzing the seismic response of crucial positions on the bridge, it became evident that the arch foot of the bridge is more susceptible to the spatial variability in near-fault ground motion, whereas the vault experiences a greater impact from the high-energy velocity pulse. Specifically, under nonuniform seismic conditions, the internal force at the base of the bridge arch increased significantly, averaging a rise of 18.69% compared to uniform excitation conditions. Conversely, the displacement and internal force response at the top of the arch exhibited more modest increases of 6.48% and 10.33%, respectively. Under nonuniform excitation, the vault’s response to near-fault earthquakes increased by an average of 20.35% com-pared to far-field earthquakes, while the arch foot’s response rose by 11.55%. In contrast, under uniform excitation, the vault’s response to near-fault earthquakes was notably higher, increasing by 25.04%, while the arch foot’s response showed a minor increase of only 2.28%. The study has revealed significant differences in the sensitivity of different parts of long-span arch bridges to near-fault earthquake characteristics. This finding is of great importance for understanding the behavior of long-span arch bridges under complex earthquake conditions. Specifically, the arch foot of the bridge is more sensitive to the spatial variability of near-fault ground motions, while the arch crown is more significantly affected by high-energy velocity pulses, providing new insights for bridge seismic design. Furthermore, the differences in response between the arch crown and arch foot under different earthquake excitations also reveal the complexity and diversity of bridge structural responses. Full article

Movement sonification has emerged as a promising approach for rehabilitation and motion control. Despite significant advancements in sensor technologies, challenges remain in developing cost-effective, user-friendly, and reliable systems for gait detection and sonification. This study introduces a novel wearable personalised sonification and biofeedback device to enhance movement awareness for individuals with irregular gait and posture. Through the integration of inertial measurement units (IMUs), MATLAB, and sophisticated audio feedback mechanisms, the device offers real-time, intuitive cues to facilitate gait correction and improve functional mobility. Utilising a single wearable sensor attached to the L4 vertebrae, the system captures kinematic parameters to generate auditory feedback through discrete and continuous tones corresponding to heel strike events and sagittal plane rotations. A preliminary test that involved 20 participants under various audio feedback conditions was conducted to assess the system’s accuracy, reliability, and user synchronisation. The results indicate a promising improvement in movement awareness facilitated by auditory cues. This suggests a potential for enhancing gait and balance, particularly beneficial for individuals with compromised gait or those undergoing a rehabilitation process. This paper details the development process, experimental setup, and initial findings, discussing the integration challenges and future research directions. It also presents a novel approach to providing real-time feedback to participants about their balance, potentially enabling them to make immediate adjustments to their posture and movement. Future research should evaluate this method in varied real-world settings and populations, including the elderly and individuals with Parkinson’s disease. Full article