Spatial Awareness Research Articles

Design and Application of Basketball Tactical Drill System Based on Motion Trajectory Analysis Algorithm

A basketball tactical drill system based on motion trajectory analysis algorithms employs advanced computational methods to enhance training effectiveness and strategy development. By utilizing these algorithms to analyze player movements and interactions during drills, coaches can gain valuable insights into team dynamics, spatial awareness, and decision-making processes. This analysis enables coaches to tailor drills to specific tactical objectives, such as improving defensive rotations, offensive spacing, or fast break execution. Additionally, the system can provide real-time feedback to players, helping them understand their positioning and movement patterns to refine their skills and decision-making abilities. This paper presents the design and implementation of a Basketball Tactical Drill System leveraging advanced object detection and motion trajectory analysis algorithms. The system aims to enhance basketball training by providing coaches and players with real-time insights into tactical drills and player performance. Two key algorithms, Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT), are utilized to accurately identify and track basketballs, players, and other relevant objects during training sessions. Experimental results demonstrate the effectiveness of the proposed system, showcasing high detection accuracy, minimal false positives, and competitive processing times across various scenarios and tactical drills. The system leverages Mean-Shift and Difference Object Detection with Motion Trajectory (DOD-MT) algorithms to achieve exceptional detection accuracy, with an average of 93% across various scenarios and tactical drills. False positives are minimized, with an average of 12 per scenario. Processing times remain competitive, averaging 27 milliseconds per scenario. Integration of motion trajectory analysis provides coaches with invaluable real-time insights into player movements and interactions, facilitating data-driven coaching strategies. The system's versatility is demonstrated through successful application in a range of drills, resulting in performance improvements averaging 20%.

Two-Line Element Outlier and Space Event Detection Method Based on Multi-Strategy Genetic Algorithm

The detection of two-line element (TLE) outliers and space events play a crucial role in enhancing spatial situational awareness. Therefore, this paper addresses the issue of TLE outlier detection methods that often overlook the mutual influence of multiple factors. Hence, a Multivariate Gaussian Mixture Model (MGMM) is introduced to consider the interdependencies among various indicators. Additionally, a Multi-strategy Genetic Algorithm (MGA) is employed to adjust the complexity of the MGMM, allowing it to accurately learn the actual distribution of TLE data. Initially, the proposed method applies probabilistic fits to the predicted error rate changes for both the TLE semi-major axis and the orbital inclination. Chaos initialization, a posterior probability penalty, and local optimization iterations are subsequently integrated into the genetic algorithm. These enhancements aim to estimate the MGMM parameters, addressing issues related to poor robustness and the susceptibility of the MGMM to converge to local optima. The algorithm’s effectiveness is validated using TLE data from typical space targets. The results demonstrate that the optimized algorithm can efficiently detect outliers and maneuver events within complex TLE data. Notably, the comprehensive detection performance index, measured, using the F1 score, improved by 15.9% compared to the Gaussian mixture model. This significant improvement underscores the importance of the proposed method in bolstering the security of complex space environments.

A Hybrid CNN-LSTM Approach for Precision Deepfake Image Detection Based on Transfer Learning

The detection of deepfake images and videos is a critical concern in social communication due to the widespread utilization of deepfake techniques. The prevalence of these methods poses risks to trust and authenticity across various domains, emphasizing the importance of identifying fake faces for security and preventing socio-political issues. In the digital media era, deep learning outperforms traditional image processing methods in deepfake detection, underscoring its significance. This research introduces an innovative approach for detecting deepfake images by employing transfer learning in a hybrid architecture that combines convolutional neural networks (CNNs) and long short-term memory (LSTM). The hybrid CNN-LSTM model exhibits promise in combating deep fakes by merging the spatial awareness of CNNs with the temporal context understanding of LSTMs. Demonstrating effective performance on open-source datasets like “DFDC” and “Ciplab”, the proposed method achieves an impressive precision of 98.21%, indicating its capability to accurately identify deepfake images with a limited false-positive rate. The model’s error rate is 0.26%, emphasizing the challenges and intricacies inherent in deepfake detection tasks. These findings underscore the potential of hybrid deep learning techniques for addressing the urgent issue of deepfake image detection.

Innovative Application of Computer Vision and Motion Tracking Technology in Sports Training

The use of cutting-edge technology has resulted in a significant enhancement in athletic training. Computer vision and motion tracking are very important for enhancing performance, reducing the risk of accidents, and training in general. Some computer vision algorithms investigate how a sportsperson moves when competing or practising. It is possible that coaches who continuously evaluate their players’ posture, muscle activation, and joint angles would have a better understanding of biomechanical efficiency. It is possible to generate performance measurements from the real-time surveillance of athletes while competing in sports. Through the use of computer vision, it is possible to identify acts that might be hazardous. Notifications are given to coaches if there is a deviation in the form of an athlete, which enables them to address the situation as soon as possible. The three variables that these sensors monitor are the direction, speed, and acceleration. Athletes can encounter realistic environments thanks to the integration of motion tracking with virtual reality. One may use the feedback loop to increase their spatial awareness and decision-making ability. Augmented reality allows for enhancing an athlete’s eyesight by providing them with real-time data while practising. Last but not least, the use of computer vision and motion tracking is bringing about a significant improvement in the sporting training process. Through collaborative efforts, researchers, athletes, and coaches can accelerate humans' performance to levels that have never been seen before.

Level of Instructional Domains on The Self- Efficacy of Gymanstics Teachers

Gymnastics is a sports activity that involves basic to challenging movements, physical exercises, and evaluation. It comprises of many forms of movements such as endurance, strength, flexibility, agility, and coordination, conducted on the floor or with the use of apparatus . As a physical training method, gymnastics improves functional body movement and teaches balance and spatial awareness, such as movement displacement, floated, balance, and weight transfer. Additionally, gymnastics offers the opportunity to create movements against gravity, enhancing fitness and skills. Moreover, gymnastics strengthens the body, maintains good physical condition, and develops good sports habits as well as improves learners' cognitive skills, shapes the body, promotes mental health, self-development, and lifelong sports consciousness. Gymnastics promotes various health and fitness benefits, well-being, and sporting prowess, making it an essential component of physical education.

Study on the Spatial Narrative of Li Changdong's Green Film "Trilogy"

Space is one of the important areas of film industry research. It is not only an important medium to simply understand the relationship between film and social reality, but also an important channel to read artists' thoughts and analyze what artists want to express and how to express them. South Korean director Lee Chang-dong is known as the "poet who asks for life" and his unique spatial awareness builds a personal influence vein full of writing. The space in Lee Chang-dong's films not only records the growth of individuals and the changes of cities, but also becomes a place where various powers compete with each other, showing the painful memories of the oppression of individuals by The Times.First of all, the paper divides the film space of Li Changdong into visual narrative space and auditory supplementary narrative space. By studying different spatial characteristics and presentation ways, it brings people's thinking behind it. Secondly, starting from the narrative function of the film, this paper studies how the space successfully participates in the narrative and becomes the narrative driving force of the film, thus affecting the narrative structure of the film and participating in the character modeling in the film. Thirdly, through some artistic languages such as sound and color, an orderly space environment is created. Finally, the paper combines the realistic point of view to study the unique spatial consciousness of director Li Changdong from his own point of view to analyze the significance behind the spatial narrative.In this paper, the spatial types, narrative functions, narrative techniques and the meaning behind Li Changdong's films are studied. Through the study of spatial narrative, I want to understand the film style and main ideas of director Li Changdong, and understand personal survival mode and living soil behind it through the entry point of space.

Translating Words to Worlds: Zero-Shot Synthesis of 3D Terrain from Textual Descriptions Using Large Language Models

The current research on text-guided 3D synthesis predominantly utilizes complex diffusion models, posing significant challenges in tasks like terrain generation. This study ventures into the direct synthesis of text-to-3D terrain in a zero-shot fashion, circumventing the need for diffusion models. By exploiting the large language model’s inherent spatial awareness, we innovatively formulate a method to update existing 3D models through text, thereby enhancing their accuracy. Specifically, we introduce a Gaussian–Voronoi map data structure that converts simplistic map summaries into detailed terrain heightmaps. Employing a chain-of-thought behavior tree approach, which combines action chains and thought trees, the model is guided to analyze a variety of textual inputs and extract relevant terrain data, effectively bridging the gap between textual descriptions and 3D models. Furthermore, we develop a text–terrain re-editing technique utilizing multiagent reasoning, allowing for the dynamic update of the terrain’s representational structure. Our experimental results indicate that this method proficiently interprets the spatial information embedded in the text and generates controllable 3D terrains with superior visual quality.

Roadside experiences of parents of children with developmental coordination disorder and/or attention deficit hyperactivity disorder.

Pedestrians are a vulnerable group at the roadside and previous research has identified that children with DCD and ADHD are at a heightened risk of pedestrian injuries. Despite this, limited research has explored parental perspectives of the pedestrian risks faced by children with DCD and/or ADHD. Understanding parents' perspectives provides a unique insight into the challenges children face every day and the concerns that parents perceive regarding their children's safety as pedestrians. Therefore, the aim of this study was to explore parents' perspectives of the pedestrian risks faced by their children with DCD and/or ADHD. Semi-structured interviews were conducted with 14 parents of primary school and early secondary school aged children with age range 7-17. The participants were divided into three groups based on their children's conditions: DCD group (10-17 years, n = 3), ADHD group (7-13 years, n = 5), and co-occurring group (7-16 years, n = 6). All parents confirmed an existing diagnosis and completed the SNAP-IV and DCDQ as screening tools. The interviews explored parents' perspectives regarding their children's pedestrian behaviors, parents' concerns and preventative measures taken to improve the pedestrian safety of their children with DCD and/or ADHD. Reflexive thematic analysis was undertaken to analyze the interviews, from which three themes were developed. The first theme related to the challenges experienced by children at the roadside; parents emphasized the significance of structured and controlled pedestrian crossing sites, underlining their preference for designated crossings as safer options due to their heightened perceptions of risk associated with other road-crossing locations. The second theme: parental concerns and influences on children's road safety referred to their children's performance and safety at the roadside, leading to increased monitoring and a more protective approach to road crossing. The third theme: road safety education related to various strategies parents implemented to mitigate risks, while balancing independence and prioritizing their safety. While there were commonalities in the challenges faced by children with DCD and/or ADHD at the roadside, there were also notable differences. Parents of children with DCD discussed challenges with spatial awareness and motor skills, whereas parents of children with ADHD discussed challenges with impulsivity and inattention. Parents of children with co-occurring DCD and ADHD described a complex interplay of these challenges. It is evident from the interviews that children with DCD and/or ADHD require a distinct approach to develop their pedestrian skills effectively and parents reported specific strategies they used to address the risks associated with their children's roadside behavior. Promoting pedestrian safety for children with DCD and/or ADHD necessitates collaboration among parents, schools and local authorities to implement comprehensive measures ensuring their safety. These findings contribute to understanding parental experiences and needs, providing valuable guidance for targeted interventions and policies to enhance the road safety of children with DCD and/or ADHD.

GBC-BCD: an improved bridge crack detection method based on bidirectional Laplacian pyramid structure with lightweight attention mechanism convolution

ABSTRACT As roads continue to expand globally, the construction of a larger number of bridges has significantly increased the burden on bridge maintenance. Traditional methods for bridge crack detection often suffer from low efficiency and accuracy. To address these issues, we propose an efficient one-stage bridge crack detection method called GBC-BCD based on the YOLOv8n framework. To improve the model’s performance, we introduce the Coordinated Attention mechanism to enhance spatial context awareness and the Bidirectional Feature Pyramid Network to strengthen the ability of feature fusion and multi-scale feature learning. At the same time, in order to make the model more efficient while improving its performance, we introduce the Ghost Module, which significantly reduces the size of the model and raises the speed of object detection. Moreover, we employ transfer learning to improve training efficiency and conserve computational resources for small datasets. Experiments show that our model has the characteristics of lightweight and high speed. Compared with famous lightweight object detection algorithms, the model size is at most reduced by 86.6%, and realises real-time (625 FPS) processing of images ( 640 × 640 ), an 8.3 times increment in speed.

The Spatial Awareness Behind the Naming of Social Groups and Geographical Areas in Mainland South Sulawesi

In the sociological paradigm of space, as a place whatever its form and name as long as it is inhabited or used as a base for human activity, it is not a passive natural entity but rather dynamic. This is made possible by the spatial dialectics and social reproduction that take place accompanying the development of a space along with the meaning of it which is always changing and dynamic. This paper examines spatial identity in naming social groups and geographical areas in Sulawesi using place identity theory, social identity theory, and identity process theory. This type of research is a literature review with a sociology of space approach that uses data and analysis descriptively from a number of relevant literature. The results showed that some social group names in Sulawesi are a representation of the geographical structure of a space, so that when mentioning the name of the group or its residential center, it will reflect its spatial identity. This fact is sociologically a spatial awareness that has been created in the long history of Sulawesi as well as can be categorized as local community intelligence

Basic Analysis of Physical Determinants Affecting the Distribution Density of Senior Citizen Centers around Small Apartment Complexes, Focusing on Administrative Districts in Busan

The role of senior citizen centers is becoming more important, with a greater emphasis placed on ensuring that these establishments facilitate leisure and communication for older adults. These developments are taking place as population aging has now become an irreversible global trend. However, there is still a lack of systematic research on predicting the distribution density of senior citizen centers based on physical factors in urban planning. Therefore, this study set each administrative district in Busan as a unit and investigated physical factors affecting the distribution density of senior citizen centers around small apartment complexes to validate their effectiveness. First, the spatial hierarchy of each administrative district in Busan was examined. The city was divided into administrative districts while focusing on the distribution density of senior citizen centers (the dependent variable) around small apartment complexes where older adults live (within a 500 m radius). The spatial accessibility of senior citizen centers and the number of apartments in each administrative district were set as independent variables. This selection was made to verify the effectiveness of the physical factors by conducting an independent sample t-test, normality test, Friedman test, and two-way ANOVA. The chief findings of this study are as follows. (1) The spatial awareness of each administrative district was low, and there were large disparities in the land development density relative to the spatial scale of the administrative districts. (2) Regarding the physical factors affecting the distribution density of senior citizen centers, the spatial accessibility of senior residences was more significant than the number of small apartment complexes nearby. (3) Personal and social factors may have indirectly influenced the distribution density of senior citizen centers more than physical factors, depending on the type of house in which the older adults live. The findings will provide a theoretical basis for determining the location and distribution density of senior citizen centers in urban planning considering physical factors, as well as serve as a reference for public policy decisions related to the allocation of such centers in the future.

Less Is More: Label Recommendation for Weakly Supervised Point Cloud Semantic Segmentation

Weak supervision has proven to be an effective strategy for reducing the burden of annotating semantic segmentation tasks in 3D space. However, unconstrained or heuristic weakly supervised annotation forms may lead to suboptimal label efficiency. To address this issue, we propose a novel label recommendation framework for weakly supervised point cloud semantic segmentation. Distinct from pre-training and active learning, the label recommendation framework consists of three stages: inductive bias learning, recommendations for points to be labeled, and point cloud semantic segmentation learning. In practice, we first introduce the point cloud upsampling task to induct inductive bias from structural information. During the recommendation stage, we present a cross-scene clustering strategy to generate centers of clustering as recommended points. Then we introduce a recommended point positions attention module LabelAttention to model the long-range dependency under sparse annotations. Additionally, we employ position encoding to enhance the spatial awareness of semantic features. Throughout the framework, the useful information obtained from inductive bias learning is propagated to subsequent semantic segmentation networks in the form of label positions. Experimental results demonstrate that our framework outperforms weakly supervised point cloud semantic segmentation methods and other methods for labeling efficiency on S3DIS and ScanNetV2, even at an extremely low label rate.

Dancing Presence and Awareness:

In this article, I argue that the dance practice of ‘Khaita – Joyful Dances’ has been intentionally designed as a mindfulness practice with the aim of fostering presence and awareness by the Dzogchen master Namkhai Norbu. The three Khaita principles of awareness (Dems, Gyu and Drig) revolve around aspects of proprioception, smooth movements, musicality, spatial and group awareness and guide dancers to the here and now. They demand non-judgmental, purposeful and non-reactive attention, hence mindfulness, that results in more mature states of awareness and presence

Shedding Light on Pre-Service Science Teachers' Instructional Preferences and Hurdles in Teaching Phases of the Moon

Purpose: This study aimed to investigate the instructional methods employed by pre-service science teachers and explore the difficulties they encountered while teaching the concept of moon phases. Design/Methodology/Approach: The present study employed a qualitative research approach using case study methodology to explore the experiences of eight pre-service science teachers. Data were collected through observation of instruction, examination of lesson plans, and semi-structured interviews to gain insights into their instructional practices and challenges. Specifically, the study aimed to understand the difficulties encountered by these pre-service teachers when teaching the phases of the moon. This approach allowed for a rich and in-depth understanding of the complex nature of teaching science to pre-service teachers. Findings: Analysis revealed that pre-service science teachers predominantly favored physical models and simulations for teaching moon phases. However, the study also identified challenges faced by these teachers in implementing topic-specific teaching strategies. Highlights: These issues highlight the importance of equipping pre-service science teachers with adequate preparation and resources to effectively teach the phases of the moon. Addressing these concerns may involve providing accurate models, instructing on proper terminology, and offering activities that foster spatial awareness to enhance their understanding and teaching abilities.

VR based gesture elicitation for user—Interfaces with low vision

<p>User interfaces (UI) and menus in virtual reality (VR), which frequently replicate traditional UI for computers and smartphones, are not created factoring for individuals with low eyesight as they demand accurate pointing and good vision to engage effectively. As an alternative method of user interaction with UI, using gestures can be recommended. Comparing gesture-based interaction with the conventional point-and-click technique for changing system settings like volume, brightness, and window manipulation in order to test this hypothesis is employed. Accessibility, spatial awareness, and precision for those with low vision while lowering cognitive load and enhancing immersion for all users can be improved by leveraging gestures. The objective of the research work is to explore the framework of Gesture Elicitation in VR environments for users with low vision. In this research work the usage of gestures as a more effective and immersive means of interacting with menus, which will not only enhance the experience of normal VR users but also drastically reduce the friction experienced by those with visual impairments is proposed. User studies demonstrate a noticeable improvement in the aforementioned areas, with faster work completion times, more immersion, and better user satisfaction.</p>

Virtual Reality‐Enabled Intuitive Magnetic Manipulation of Microrobots and Nanoparticles

Magnetic microrobots and nanoparticles offer unique capabilities for medical applications by granting unprecedented access to intricate and delicate anatomical structures. Nevertheless, an intuitive manipulation approach remains a challenge due to their small size and limited feedback. This study presents a new method for magnetic microrobot and nanoparticle control that employs virtual reality (VR), creating an immersive and realistic view of the bodily anatomy and the microrobot within. This study compares three manipulation modes: a traditional mode using 2D displays, VR mode using a VR headset and touch controllers, and VR autonomous programmed maneuvering. It is shown that VR‐assisted modes reduce the manipulation time by improving spatial awareness. Nanoparticles in a VR environment are also manipulated. The proposed method will find applications in terms of intuitive, immersive microrobots or magnetic nanoparticle control in complex biological environments, thus in many medical procedures and for drug and cell delivery. The immersive nature of this approach enhances the ability of the user to perceive and understand complex anatomical structures, facilitating better navigation within delicate environments; both dexterity and spatial awareness are improved. Thus, it is shown how microrobots and magnetic nanoparticles can be controlled using immersive simulations to improve both visualization and manipulation.

ResNet50-Boosted UNet for Improved Liver Segmentation Accuracy

Segmentation of the liver from abdominal CT images is difficult due to changes in form, density, and the presence of malignancies. This research describes a novel strategy to improve segmentation accuracy that uses UNet as a foundation architecture and ResNet50 as a backbone architecture. This integrated design automates feature selection and spatial awareness, overcoming limitations in previous models. Experimental evaluations using the LiTS dataset show higher performance. Specifically, using the LiTS dataset, our algorithm achieves a remarkable foreground accuracy of 99.81% in liver segmentation. These results outperform existing approaches, demonstrating UNet and ResNet50's potential as valuable tools for precise liver segmentation in clinical situations. The suggested system shows promise for application in diverse medical imaging tasks other than liver segmentation, demonstrating its versatility and effectiveness in enhancing machine-assisted medical diagnostics and decision-making processes.

Using Eye-Tracking for Adaptive Human-Machine Interfaces for Pilots: A Literature Review and Sample Cases

This paper explores the potential of eye-tracking technology in adaptive human-machine interfaces for pilots in aviation. We argue that an interface able to adjust its layout and elements based on pilots’ real-time eye-tracking data can prevent errors and enhance their performance. The study presents a literature review on the use of eye-tracking for various pilot cases, including flight simulator games, drone pilots, and cockpit pilots. Results in most cases showed that eye-tracking has been employed to improve interactions, enhance spatial awareness, guide pilots’ gaze to relevant areas, and provide insights into pilots’ information processing and task load. The paper discusses two sample cases demonstrating the potential of eye-tracking in adaptive human-machine interfaces. In the first case, during challenging drone simulations, eye-tracking identified areas where an adaptive human-machine interface could aid navigation and reduce cognitive load. In the second one, based on real drone flights, when signal loss incidents occurred, eye-tracking data showed that the interface should adapt to pilots’ needs by providing critical information to help them to improve situational awareness. The paper concludes that eye-tracking technology has significant potential in adaptive human-machine interfaces for aviation, emphasising the importance of refining these technologies to meet pilots’ specific needs and enhance flight safety.

Does hearing impairment affect gait under realistic conditions?

Hearing loss is associated with increased risk of falling, but little is known about how hearing impairment might affect mobility. Here, we report preliminary results from an ongoing experimental study investigating dynamic properties of gait while walking with and without impaired hearing. Young- and middle-aged adult participants with self-reported normal hearing completed four walking tasks, 2 indoors, 2 outdoors, each conducted with and without a simulated hearing loss. In the impaired condition, participants wore an insert earplug in one ear combined with binaural, circumaural noise-damping headphones. We quantified gait parameters using data from inertial measurement units (IMUs) affixed to participants’ ankles and waist, allowing participants to walk farther than in typical gait assessments. Parameters included standard gait metrics, such as limb acceleration as well as a novel spatiotemporal index quantifying variability in step patterns. Data have been collected from 13 of 30 projected participants. Analysis will focus on how temporal-spatial gait parameters and variability differ between indoor and outdoor walking with and without hearing impairment. We expect gait parameters to reflect a more variable and potentially less mobile walking strategy in the outdoor condition and with impaired hearing due to increased cognitive load and/or reduced spatial awareness in those conditions.

Wayfinding in immersive virtual environments as social activity supported by virtual agents

Effective navigation and interaction within immersive virtual environments (IVEs) rely on thorough scene exploration. Therefore, wayfinding is essential, assisting users in comprehending their surroundings, planning routes, and making informed decisions. Based on real-life observations, wayfinding is, thereby, not only a cognitive process but also a social activity profoundly influenced by the presence and behaviors of others. In virtual environments, these “others” are virtual agents (VAs), defined as anthropomorphic computer-controlled characters, who enliven the environment and can serve as background characters or direct interaction partners. However, less research has been conducted to explore how to efficiently use VAs as social wayfinding support. In this paper, we aim to assess and contrast user experience, user comfort, and acquisition of scene knowledge through a between-subjects study involving n = 60 participants across three distinct wayfinding conditions in one slightly populated urban environment: (i) unsupported wayfinding, (ii) strong social wayfinding using a virtual supporter who incorporates guiding and accompanying elements while directly impacting the participants’ wayfinding decisions, and (iii) weak social wayfinding using flows of VAs that subtly influence the participants’ wayfinding decisions by their locomotion behavior. Our work is the first to compare the impact of VAs’ behavior in virtual reality on users’ scene exploration, including spatial awareness, scene comprehension, and comfort. The results show the general utility of social wayfinding support, while underscoring the superiority of the strong type. Nevertheless, further exploration of weak social wayfinding as a promising technique is needed. Thus, our work contributes to the enhancement of VAs as advanced user interfaces, increasing user acceptance and usability.