Movement Of Objects Research Articles

An Investigation of Running Kinematics with Recovered Anterior Cruciate Ligament Reconstruction on a Treadmill and In-Field Using Inertial Measurement Units: A Preliminary Study.

Anterior cruciate ligament reconstruction (ACLR) may affect movement even years after surgery. The purpose of this study was to determine possible interlimb asymmetries due to ACLR when running on a treadmill and in field conditions, with the aim of contributing to the establishment of objective movement assessment in real-world settings; moreover, we aimed to gain knowledge on recovered ACLR as a biomechanical risk factor. Eight subjects with a history of unilateral ACLR 5.4 ± 2.8 years after surgery and eight healthy subjects ran 1 km on a treadmill and 1 km on a concrete track. The ground contact time and triaxial peak tibial accelerations were recorded using inertial measurement units. Interlimb differences within subjects were tested and compared between conditions. There were no significant differences between limbs in the ACLR subjects or in healthy runners for any of the chosen parameters on both running surfaces. However, peak tibial accelerations were higher during field running (p-values < 0.01; Cohen's d effect sizes > 0.8), independent of health status. To minimize limb loading due to higher impacts during field running, this should be considered when choosing a running surface, especially in rehabilitation or when running with a minor injury or health issues.

System for Detecting Moving Objects Using 3D Li-DAR Technology

The “System for Detecting Moving Objects Using 3D LiDAR Technology” introduces a groundbreaking method for precisely identifying and tracking dynamic entities within a given environment. By harnessing the capabilities of advanced 3D LiDAR (Light Detection and Ranging) technology, the system constructs intricate three-dimensional maps of the surroundings using laser beams. Through continuous analysis of the evolving data, the system adeptly discerns and monitors the movement of objects within its designated area. What sets this system apart is its innovative integration of a multi-sensor approach, combining LiDAR data with inputs from various other sensor modalities. This fusion of data not only enhances accuracy but also significantly boosts the system’s adaptability, ensuring robust performance even in challenging environmental conditions characterized by low visibility or erratic movement patterns. This pioneering approach fundamentally improves the precision and reliability of moving object detection, thereby offering a valuable solution for a diverse array of applications, including autonomous vehicles, surveillance, and robotics.

Dynamic Object Detection in Surveillance Videos using Temporal Convolutional Networks and Federated Learning in Edge Computing Environments

This research addresses the importance of advancing dynamic object detection in surveillance videos by introducing a novel framework that integrates Temporal Convolutional Networks (TCNs) and Federated Learning (FL) within edge computing environments. This research is motivated by the critical need for real-time threat response, enhanced security measures, and privacy preservation in dynamic surveillance scenarios. Leveraging TCNs, the system captures temporal dependencies, providing a comprehensive understanding of object movements. FL ensures decentralized model training, mitigating privacy concerns associated with centralized approaches. Current challenges in real-time processing, privacy preservation, and adaptability to dynamic environments are addressed through innovative solutions. Model optimization techniques optimize TCN efficiency, ensuring real-time processing. Advanced privacy-preserving mechanisms secure FL, addressing privacy concerns. Transfer learning and data augmentation enhance adaptability to dynamic scenarios. The proposed system not only addresses existing challenges but also contributes to the evolution of surveillance technology. Comprehensive metrics, including accuracy, sensitivity, specificity, and real-time processing metrics, provide a thorough evaluation of the system's performance. This research introduces an approach to dynamic object detection, combining TCN and FL in edge computing environments. Results show accuracy exceeding 97%, sensitivity and specificity at 97% and 98%, and F1 score reaching 96%.

Using Case Forms for Expressing Temporal Relations in Modern Turkish Language

Many languages, including modern Turkish, employ lexemes with spatial meaning to express temporal relations. However, the Turkish language stands out for its use of case forms of nouns both to describe the spatial position and movement of objects and to establish the temporal reference of actions and events. These differences are systemic, suggest that native Turkish speakers perceive spatial and temporal phenomena in a unique way, and determine the historical change in their models of time perception. They challenge the belief that spatial relations prevail over temporal ones in the perception of the surrounding world by peoples with nomadic roots but highlight the necessity of considering materials from other Turkic languages. The results obtained show that facts from different linguistic levels should be involved to analyze how temporal relations are conveyed.

Eco-Friendly Material Handling Systems for Industries Using Maglev Technology

Magnetic Levitation (Maglev) technology has transformed material handling by offering an efficient, contactless method for object transport. This innovation relies on electromagnetic repulsion and stabilization, eliminating the need for traditional mechanical systems. Through powerful magnetic fields and superconducting materials, Maglev systems effortlessly suspend and propel objects with minimal friction and energy consumption. This review delves into Maglev's advancements and applications in material handling, exploring fundamental principles like electromagnetic repulsion and stability control. It emphasizes benefits such as reduced maintenance, increased throughput, and enhanced safety. The article provides insights into Maglev's applications, spanning warehouse automation, assembly line transportation, and high-speed logistics. It highlights how Maglev systems elevate operational efficiency, optimize space, and deliver precise control over object movement, resulting in faster and more reliable material handling processes.

3D Convolutional Neural Networks based Movement Evaluation System for Gymnasts in Computer Vision Applications

This study introduces an innovative Movement Evaluation System, which utilizes state of art 3D CNNs in grassland of computer vision. This system presents a significant advancement in analyzing and assessing complex gymnastic movements, providing a comprehensive understanding of both spatial and temporal dynamics. By incorporating pose estimation algorithms, it accurately identifies body joints and positions to extract detailed spatial features. The 3D CNN further captures the temporal evolution of these features, enabling a precise analysis of the fluidity, rhythm, and synchronization of gymnastic movements over time. The effectiveness of this system is evaluated through performance metrics such as precision, recall, and accuracy. Aimed at enhancing sports science and athlete training, this research offers coaches, judges, and gymnasts a sophisticated tool for objective and standardized movement assessments. It has the potential to streamline the evaluation process in gymnastics and provide constructive feedback efficiently. The integration of 3D CNNs in this system marks a paradigm shift in utilizing advanced computer vision techniques to improve the understanding and refinement of gymnastic performances.

Proximal groups: Extension of topological groups. Application in the concise representation of Hilbert envelopes on oscillatory motion waveforms

In this paper, we introduce proximal groups that are a generalization of topological groups. A straight-forward application of proximal groups is given in the concise representation of collections of overlapping Hilbert envelope lobes attached to the peak points on oscillatory motion waveforms that occur in sequences of video frames that track object movements.

A comprehensive approach to characterize navigation instruments for magnetic guidance in biological systems

Achieving non-invasive spatiotemporal control over cellular functions, tissue organization, and behavior is a desirable aim for advanced therapies. Magnetic fields, due to their negligible interaction with biological matter, are promising for in vitro and in vivo applications, even in deep tissues. Particularly, the remote manipulation of paramagnetic (including superparamagnetic and ferromagnetic, all with a positive magnetic susceptibility) entities through magnetic instruments has emerged as a promising approach across various biological contexts. However, variations in the properties and descriptions of these instruments have led to a lack of reproducibility and comparability among studies. This article addresses the need for standardizing the characterization of magnetic instruments, with a specific focus on their ability to control the movement of paramagnetic objects within organisms. While it is well known that the force exerted on magnetic particles depends on the spatial variation (gradient) of the magnetic field, the magnitude of the field is often overlooked in the literature. Therefore, we comprehensively analyze and discuss both actors and propose a novel descriptor, termed ‘effective gradient’, which combines both dependencies. To illustrate the importance of both factors, we characterize different magnet systems and relate them to experiments involving superparamagnetic nanoparticles. This standardization effort aims to enhance the reproducibility and comparability of studies utilizing magnetic instruments for biological applications.

Objective movement asymmetry in horses is comparable between markerless technology and sensor-based systems.

A markerless artificial intelligence (AI) system for lameness detection has recently become available but has not been extensively compared with commonly used inertial measurement unit (IMU) systems for detecting asymmetry under field conditions. Comparison of classification of asymmetric limbs under field conditions and comparison of normalised asymmetry data using a markerless AI system (SleipAI; recorded on a tripod mounted iPhone 14pro [SL]); the Equinosis Q Lameness Locator (LL); the EquiMoves (EM); and subjective evaluation (SE). Descriptive clinical study. Straight line trot data were collected from 52 client-owned horses in regular training. Limbs were categorised as symmetric or asymmetric. Number of analysed strides were compared with Wilcoxon's each pairs test. Inter-rater reliability in classification of asymmetric limbs was assessed with Light's Kappa. Bland Altman analysis of normalised asymmetry data was performed. Data from 41 horses were included. Most horses showed mild asymmetry. The EM analysed significantly more strides than the other systems, both for forelimbs and for hindlimbs (53 ± 11 strides for both, respectively; p < 0.006). The LL analysed significantly more hindlimbs strides (45 ± 13) than the SL (27 ± 6; p < 0.001). Moderate inter-rater agreement for asymmetry classification was found between systems (k = 0.59 forelimbs; 0.44 hindlimbs); agreement decreased when including the SE. For the normalised asymmetry data, the strongest agreement was found between the two IMU systems. Horses were assessed during straight-line trot only. The objective systems were comparable in classification of asymmetric limbs under field conditions when using defined asymmetry thresholds. Discrepancies stemmed largely from the imposed thresholds (i.e., systems largely identified same-side asymmetry). Overall, the strongest agreement was found between LL and EM. The SL analysed significantly fewer hindlimb strides than the LL and EM which could represent a limitation of the Sleip AI.

Model-based feedforward control for an optimized manipulation of acoustically levitated spheres

We present a simple dynamic model for predicting the manipulation behavior of an acoustically levitated sphere. The model allows for the calculation of the sphere position over time, which is demonstrated for two manipulation strategies: a straight motion with a constant manipulation velocity and a straight motion in which the sphere acceleration follows a cosine function. The dynamic model as well as the manipulation strategies is verified experimentally in an acoustic levitator system consisting of an array of 16 by 16 ultrasonic transducers emitting at 40 kHz and an opposing reflector. In this system, a glass sphere of a diameter of 2 mm is manipulated horizontally by controlling the phases of the transducers. The sphere motion is recorded using a high-speed camera, and a tracking algorithm is used for capturing the sphere position over time. Moreover, a model predictive control algorithm is applied on a path-following problem to move the sphere along a given reference trajectory by means of a model-based optimal feedforward control. The proposed dynamic model as well as the methodology presented in this paper enables faster manipulation speeds with reduced oscillations during object movement.

Effect of fluid–structure interaction on the oblique water entry of the projectile under the influence of floating ice structure

The water entry of a projectile constrained by polar floating ice presents a unique cross-media challenge. This paper investigates the dynamics of oblique water entry for a projectile influenced by floating ice using the fluid–structure interaction (FSI) method. The validity of the numerical method has been confirmed through experimental validation. The water entry process of a projectile from the side of the floating ice is examined. The evolution of the cavity and the movement patterns of objects as the distance between the projectile and the floating ice decreases toward collision are investigated. The influence of water on the critical collision distance between the projectile and the floating ice during oblique water entry is analyzed. Additionally, the physical mechanism of floating ice deflection through collision is investigated based on the theory of cavity dynamics. Subsequently, the study focuses on the oblique water entry process of a projectile colliding with the upper surface of the floating ice. Different entry angles determine the collision mode between the projectile and the floating ice surface. This study also examines how varying entry angles influence cavity evolution and object movement patterns during oblique collisions. Different collision modes between the projectile and the floating ice lead to asymmetric cavity evolution and various modes of object deflection motion. Finally, changes in the flow field and vortex structure during oblique collisions are studied to examine the influence of the FSI process between the projectile and the floating ice on the flow field.

A Study of YOLO-Based Object Detection for Visually Impaired Individuals

Abstract: An essential area of investigation involves recognizing and tracking objects, given the frequent changes in object motion, scene dimensions, occlusions, appearance, ego-motion, and lighting variations. Effective object tracking heavily relies on feature selection due to its critical role. This process is integral to numerous real-time applications like vehicle detection and video surveillance. To address detection challenges, tracking methods need to be adept at handling object movement and appearance changes. Among these methods, tracking algorithms play a pivotal role in smoothing video sequences and receive significant attention. However, only a few approaches leverage previously gathered data on object attributes such as shape and color. This study delves into a tracking algorithm that encompasses all these object properties. Its aim is to investigate and assess past methodologies for tracking and detecting moving objects across various stages of video sequences. Additionally, it aims to identify existing challenges and propose innovative strategies to enhance object tracking throughout video frames

Evaluation of Contemporary Art in the Process of Production, Distribution and Consumption

How does the art community evaluate contemporary art? This question is discussed based on 16 interviews with artists, gallery owners, curators, and collectors. The contemporary art market is conceptualized as a “creative industry that unites actors involved in the production, distribution, and consumption of art objects. The research methodology is based on the anthropological approach of Igor Kopytoff and the discursive psychology of Rom Harré, according to which the history of the movement of cultural objects through social space and the discourses accompanying them are sources of symbolic values. Market practices of art distribution are described: criteria for selecting artists by galleries, legal aspects of distribution and consumption of art objects, informal purchasing practices, moral and pragmatic taboos on buying art objects. Four logics of justifying the value of art objects — “commercial success,” “testimony of the times,” “intellectual development,” “emotional experience”—have been reconstructed. It is demonstrated that consumption as a collector's practice requires following the rules of a “professional approach” to be recognized in the art community.

Development and validation of a test instrument for the assessment of perceived basic motor competencies in first and second graders: the SEMOK-1-2 instrument.

Both actual motor competencies (AMC) and perceived motor competencies (PMC) play an important role in motor development research and children's physical and psychological development. PMC refer to children's perceptions of their motor competencies. To assess the PMC of first and second grade children (aged 6-9 years), the SEMOK-1-2 instrument was developed. The instrument is aligned to the validated MOBAK-1-2 instrument which assesses AMC in the competence areas "object movement" and "self-movement" Accounting for possible reading difficulties in younger children, the motor tasks and answer options were illustrated and explained verbally. The purpose of this study was to test and validate the SEMOK-1-2 instrument and investigate the associations between the constructs AMC, PMC and physical activity (PA), whereby PA was measured by the participation in team and individual sports. Data from N = 404 pupils in the German-speaking part of Switzerland from first and second grades (M = 7.8 years, SD = 0.69, 49% boys) were analyzed. Confirmatory factor analyses were conducted to test the factorial validity of the SEMOK-1-2 instrument. Structural equation models were used to investigate the association between the constructs. The analyses confirmed a two-factor structure with the factors PMC "object movement" and PMC "self-movement", corresponding to the factors existing in the MOBAK-1-2 instrument. Latent correlations between AMC factors and the corresponding PMC factors were r = 0.79 for "object movement" and r = 0.76 for "self-movement". Associations with external criteria and covariates, such as sex, were associated with both AMC and PMC. Analyses also revealed that children who participated more often in individual and team sports showed higher levels in both AMC and PMC. The confirmation of the two-factorial structure of the SEMOK-1-2 instrument and the associations between AMC and PMC as well as external criteria indicate construct and criterion validity. The SEMOK-1-2 instrument can be economically utilized for assessing PMC and is also suitable for the monitoring of PMC in the context of Physical Education.

Deep Reinforcement Learning-Empowered Cost-Effective Federated Video Surveillance Management Framework.

Video surveillance systems are integral to bolstering safety and security across multiple settings. With the advent of deep learning (DL), a specialization within machine learning (ML), these systems have been significantly augmented to facilitate DL-based video surveillance services with notable precision. Nevertheless, DL-based video surveillance services, which necessitate the tracking of object movement and motion tracking (e.g., to identify unusual object behaviors), can demand a significant portion of computational and memory resources. This includes utilizing GPU computing power for model inference and allocating GPU memory for model loading. To tackle the computational demands inherent in DL-based video surveillance, this study introduces a novel video surveillance management system designed to optimize operational efficiency. At its core, the system is built on a two-tiered edge computing architecture (i.e., client and server through socket transmission). In this architecture, the primary edge (i.e., client side) handles the initial processing tasks, such as object detection, and is connected via a Universal Serial Bus (USB) cable to the Closed-Circuit Television (CCTV) camera, directly at the source of the video feed. This immediate processing reduces the latency of data transfer by detecting objects in real time. Meanwhile, the secondary edge (i.e., server side) plays a vital role by hosting a dynamically controlling threshold module targeted at releasing DL-based models, reducing needless GPU usage. This module is a novel addition that dynamically adjusts the threshold time value required to release DL models. By dynamically optimizing this threshold, the system can effectively manage GPU usage, ensuring resources are allocated efficiently. Moreover, we utilize federated learning (FL) to streamline the training of a Long Short-Term Memory (LSTM) network for predicting imminent object appearances by amalgamating data from diverse camera sources while ensuring data privacy and optimized resource allocation. Furthermore, in contrast to the static threshold values or moving average techniques used in previous approaches for the controlling threshold module, we employ a Deep Q-Network (DQN) methodology to manage threshold values dynamically. This approach efficiently balances the trade-off between GPU memory conservation and the reloading latency of the DL model, which is enabled by incorporating LSTM-derived predictions as inputs to determine the optimal timing for releasing the DL model. The results highlight the potential of our approach to significantly improve the efficiency and effective usage of computational resources in video surveillance systems, opening the door to enhanced security in various domains.

Correlation research on visual behavior and cognition of impression against localized church facades in China

Churches in China are material witnesses of cultural dissemination, and their architectural forms are in the process of localization. In order to determine the optimal degree of localization of church facades as well as to study the correlation between visual behavior and subjective cognition, five church facades with different degrees of localization were selected in present study, and the questionnaire survey as well as eye-tracking technology were used to collect data from two aspects: subjective cognition (the impression and acceptance levels) and objective eye movement (the first fixation duration, total fixation duration, fixation count, and visit count). The results showed the differences in public perceptions of church facades, and the impression of participants was continuously enhanced with the increasing of localization degree of church facade, while the acceptance level showed a U-shaped change. What's more, the correlation between the impression level and the first fixation duration was found to be 0.910, the Pearson coefficient between the acceptance level and the total fixation duration was found to be 0.928, indicating that eye-tracking indicators could accurately reflect the subjective cognition of the public. Performed analyses demonstrated that eye-tracking technology would provides an important technical mean for the design, conservation, and renewal of building facades.

Single-shot synthetic wavelength imaging: Sub-mm precision ToF sensing with conventional CMOS sensors

We present a novel single-shot interferometric ToF camera targeted for precise 3D measurements of dynamic objects. The camera concept is based on Synthetic Wavelength Interferometry, a technique that allows retrieval of depth maps of objects with optically rough surfaces at submillimeter depth precision. In contrast to conventional ToF cameras, our device uses only off-the-shelf CCD/CMOS detectors and works at their native chip resolution (as of today, theoretically up to 20 Mp and beyond). Moreover, we can obtain a full 3D model of the object in single-shot, meaning that no temporal sequence of exposures or temporal illumination modulation (such as amplitude or frequency modulation) is necessary, which makes our camera robust against object motion.In this paper, we introduce the novel camera concept and show the first measurements that demonstrate the capabilities of our system. We present 3D measurements of small (cm-sized) objects with > 2 Mp point cloud resolution (the native pixel resolution of our used detector) and up to sub-mm depth precision. We also report a “single-shot 3D video” acquisition and a first single-shot “Non-Line-of-Sight” measurement. Our technique has great potential for high-precision applications with dynamic object movement, e.g., in AR/VR, industrial inspection, medical imaging, and imaging through scattering media like fog or human tissue.

Identification of suspicious behavior through anomalies in the tracking data of fishing vessels

Automated positioning devices can generate large datasets with information on the movement of humans, animals and objects, revealing patterns of movement, hot spots and overlaps among others. However, in the case of Automated Information Systems (AIS), attached to vessels, observed strange behaviors in the tracking datasets may come from intentional manipulation of the electronic devices. Thus, the analysis of anomalies can provide valuable information on suspicious behavior. Here, we analyze anomalies of fishing vessel trajectories obtained with the Automatic Identification System. The map of silent anomalies, those that occur when positioning data are absent for more than 24 hours, shows that they are most likely to occur closer to land, with 87.1% of anomalies observed within 100 km of the coast. This behavior suggests the potential of identifying silence anomalies as a proxy for illegal activities. With the increasing availability of high-resolution positioning of vessels and the development of powerful statistical analytical tools, we provide hints on the automatic detection of illegal activities that may help optimize the management of fishing resources.

Between Smart Images and Fast Trucks: Digital Surveillance and Obscured Labour in Hyderabad, India

In Hyderabad, India, the growing information technology (IT) sector relies on ensuring safe and efficient movements of people and objects, and the city government and private actors have embraced the promise of digital surveillance to reach these goals. The new Telangana State, created in 2014, has built a new city-wide network of smart cameras, and at ‘hackathons’ programmers develop new digital tools, often connected to this network, that will technologically ‘solve’ social problems. In this article, I examine the system of CCTV cameras and programmers’ investments in these systems, and explore how migrant Vaddera stonecutters use cellphones to evade patrolling officers monitoring the streets where they carry the granite stones that they cut and load to construct the city’s buildings. Expanding on what Gilbert Simondon calls ‘the margin of indeterminacy’, this article reveals gaps in the digital infrastructure of surveillance—even as its integration and completion combine human and technical elements.

Flow Spatiotemporal Moran's I: Measuring the Spatiotemporal Autocorrelation of Flow Data

Flows can reflect the spatiotemporal interactions or movements of geographical objects between different locations. Measuring the spatiotemporal autocorrelation of flows can help determine the overall spatiotemporal trends and local patterns. However, quantitative indicators of flows used to measure spatiotemporal autocorrelation both globally and locally are still rare. Therefore, we propose the global and local flow spatiotemporal Moran's I (FSTI). The global FSTI is used to assess the overall spatiotemporal autocorrelation degree of flows, and the local FSTI is applied to identify local spatiotemporal clusters and outliers. In the FSTI, to reflect flow spatiotemporal adjacency relationships, we establish flow spatiotemporal weights by multiplying the spatial and temporal weights of flows considering spatiotemporal orthogonality. The flow spatial weights include contiguity‐based (considering first/higher‐order and common border) and Euclidean distance‐based weights. The temporal weights consider ordinary and lagged cases. As flow attributes may follow a long‐tail distribution, we conduct Monte Carlo simulations to evaluate the statistical significance of the results. We assess the FSTI using synthetic datasets and Chinese population mobility datasets, and compare some results with those of recent flow‐related methods. Additionally, we perform a sensitivity analysis to select a suitable temporal threshold. The results show that the FSTI can be used to effectively detect spatiotemporal variations in the autocorrelation degree and type.