Video Data Collection Research Articles

Reliability and Educational Suitability of TikTok Videos asa Source of Information on Sleep and Awake Bruxism: ACross-Sectional Analysis.

TikTok contains many videos about bruxism that may help raise the level of awareness on the topic. However, the quality of these videos' information represents the greatest concern. The present cross-sectional analysis aimed to systematically assess the reliability and educational suitability of TikTok videos as a source of information on bruxism. TikTok was searched for relevant videos uploaded until 6 March 2024, using the hashtags #bruxism, #grindingteeth and #jawclenching. Independent pre-calibrated operators conducted video inclusion and data collection, noting videos' characteristics, source, popularity, content, reliability (assessed through the DISCERN scoring system) and educational value (assessed through the Global Quality Score [GQS]). The Kruskal-Wallis test was adopted to compare videos from different sources. Videos with low and high educational value were compared using the Mann-Whitney U-test. Tests were considered significant when the p-value was < 0.05. A total of 105 bruxism videos were analysed. Most videos covered bruxism management and clinical features, and more than 60% were uploaded by healthcare professionals. According to the DISCERN and GQS scoring, the videos' reliability and educational value were judged to be very poor and low, respectively, but on average they were higher for videos uploaded by healthcare professionals compared to those uploaded by laypeople. When comparing videos with high educational value to those with low educational value, differences were found in popularity, content and reliability. The present results suggest the need for greater control of information disseminated on social media to fulfil its potential role in educating laypeople about bruxism.

On-field Head Acceleration Exposure Measurements Using Instrumented Mouthguards: Multi-stage Screening to Optimize Data Quality.

Instrumented mouthguards (iMGs) are widely applied to measure head acceleration event (HAE) exposure in sports. Despite laboratory validation, on-field factors including potential sensor skull-decoupling and spurious recordings limit data accuracy. Video analysis can provide complementary information to verify sensor data but lacks quantitative kinematics reference information and suffers from subjectivity. The purpose of this study was to develop a rigorous multi-stage screening procedure, combining iMG and video as independent measurements, aimed at improving the quality of on-field HAE exposure measurements. We deployed iMGs and gathered video recordings in a complete university men's ice hockey varsity season. We developed a four-stage process that involves independent video and sensor data collection (Stage I), general screening (Stage II), cross verification (Stage III), and coupling verification (Stage IV). Stage I yielded 24,596 iMG acceleration events (AEs) and 17,098 potential video HAEs from all games. Approximately 2.5% of iMG AEs were categorized as cross-verified and coupled iMG HAEs after Stage IV, and less than 1/5 of confirmed or probable video HAEs were cross-verified with iMG data during stage III. From Stage I to IV, we observed lower peak kinematics (median peak linear acceleration from 36.0 to 10.9g; median peak angular acceleration from 3922 to 942rad/s2) and reduced high-frequency signals, indicative of potential reduction in kinematic noise. Our study proposes a rigorous process for on-field data screening and provides quantitative evidence of data quality improvements using this process. Ensuring data quality is critical in further investigation of potential brain injury risk using HAE exposure data.

The effects of analogy teaching on sport skill acquisition in children

Introduction: Teaching motor skills to novices has been the main task of physical educators, and studies in motor learning have always sought to discover better ways to optimize the skill acquisition process. Movement information is presented transparently through the performer’s demonstration, illustration, feedback, and verbal guidance in motor skills instructing. This study seeks to determine the type of learning, i.e., analogy learning or explicit learning, suitable for promoting the children’s acquisition of skills that are biomechanically and kinematically different from the same skill learned by explicit instructions, and to determine how the physical form of the skill evolves over practice. Material and Methods: Forty-five right-handed healthy beginner male students (mean age 9.93 ±0.55 years; height 1.39 ±4.16 m; body mass 31.65±3.23 kg; novice in basketball) participated in the study. Subjects were randomly allocated to an explicit learning condition (n = 15), an analogy learning condition (n = 15), or an uninstructed control condition (n = 15). Ten free throws (FT) in the standing position were performed with the right hand by each student using modified equipment i.e., a small ball, customized rim with 45-cm circumference and adapted net height of 2 m. Attempts were recorded from 3 m away in a biomechanical laboratory with two-dimensional (2D) video data collection (i.e., using 240 Hz camera resolution). Results: The independent ANOVA yielded a statistically significant effect across the three groups in post-tests: F (8,16) = 283.233, P = 0.001, η2 = 0.793. A statistically significant difference was observed across the three groups: F (8, 16) = 332.057, P = 0.001, η2 = 0.818. Discussion: The analogy group's performance was significantly better in the skill taught. The explicit learners achieved lower scores compared to the analogy learners. Conclusions: The analogy learning training method is recommended for children aged 10–12 willing to learn FT in basketball. The conclusions may be important for coaches concerning the use of different training methods in skill learning.

New interactive machine learning tool for marine image analysis.

Advancing imaging technologies are drastically increasing the rate of marine video and image data collection. Often these datasets are not analysed to their full potential as extracting information for multiple species is incredibly time-consuming. This study demonstrates the capability of the open-source interactive machine learning tool, RootPainter, to analyse large marine image datasets quickly and accurately. The ability of RootPainter to extract the presence and surface area of the cold-water coral reef associate sponge species, Mycale lingua, was tested in two datasets: 18 346 time-lapse images and 1420 remotely operated vehicle video frames. New corrective annotation metrics integrated with RootPainter allow objective assessment of when to stop model training and reduce the need for manual model validation. Three highly accurate M. lingua models were created using RootPainter, with an average dice score of 0.94 ± 0.06. Transfer learning aided the production of two of the models, increasing analysis efficiency from 6 to 16 times faster than manual annotation for time-lapse images. Surface area measurements were extracted from both datasets allowing future investigation of sponge behaviours and distributions. Moving forward, interactive machine learning tools and model sharing could dramatically increase image analysis speeds, collaborative research and our understanding of spatiotemporal patterns in biodiversity.

Expanding Education Researchers' Access to Classroom Observation Data With a Remote and Cost-Effective Video Data Collection Protocol.

The onset of the COVID-19 pandemic and associated long-term shifts to virtual instruction among most US schools presented notable challenges among education researchers. Ongoing projects conducted in school settings experienced sudden losses of access to teacher and student participants, in many cases leading to severe interruptions to data collection efforts. Perhaps most notably, upon returns to in-person instruction in the 2021/22 academic year most schools instigated strict policies limiting the number of non-school personnel who could enter school buildings, including researchers conducting in-person data collections. As such, many researchers had to find alternative means to gather data. In this paper, we offer a new protocol that we created in response to these challenges that allows for the secure and fully remote collection of video data in school settings. This new protocol not only addressed the immediate needs of the focal study but also addresses some of the most notable barriers to collecting classroom video data in the field of education research at large. In this paper, we describe the initial development and application of this protocol among a local study of elementary teachers, as well as the scaling of this protocol in a study of elementary teachers in multiple states. It is our hope that this protocol can expand education researchers', practitioners', and policymakers' access to classroom video data.

A jellyfish inspired soft robot powered by cable-driven actuation

Jellyfish’s natural propulsion abilities with a soft body has often served as an inspiration for many soft robots due to their size and efficiency, with many robots being based on the ephyra larva stage of a jellyfish. However, most previous jellyfish-inspired robots were constructed of costly actuation systems, as well as being based on the ephyra larva stage rather than an adult jellyfish. To address these issues, a jellyfish-like soft robot was made with 4 cable-driven actuators which were attached to a dome-shaped soft material, mimicking that of the jellyfish. A mechanism using a servo powered the cable-driven actuators, allowing the dome-shaped cavity to contract its bell uniformly. When actuated, the mechanism expels a jet of fluids from the bell, leading to a positive thrust. The result was empirically measured by collecting video data on the robot's performance underwater.

Coverage estimation of benthic habitat features by semantic segmentation of underwater imagery from South-eastern Baltic reefs using deep learning models

Underwater imagery (UI) is an important and sometimes the only tool for mapping hard-bottom habitats. With the development of new camera systems, from hand-held or simple “drop-down” cameras to ROV/AUV-mounted video systems, video data collection has increased considerably. However, the processing and analysing of vast amounts of imagery can become very labour-intensive, thus making it ineffective both time-wise and financially. This task could be simplified if the processes or their intermediate steps could be done automatically. Luckily, the rise of AI applications for automatic image analysis tasks in the last decade has empowered researchers with robust and effective tools. In this study, two ways to make UI analysis more efficient were tested with eight dominant visual features of the Southeastern Baltic reefs: 1) the simplification of video processing and expert annotation efforts by skipping the video mosaicking step and reducing the number of frames analysed; 2) the application of semantic segmentation of UI using deep learning models. The results showed that the annotation of individual frames provides similar results compared to 2D mosaics; moreover, the reduction of frames by 2–3 times resulted in only minor differences from the baseline. Semantic segmentation using the PSPNet model as the deep learning architecture was extensively evaluated, applying three variants of validation. The accuracy of segmentation, as measured by the intersection-over-union, was mediocre; however, estimates of visual coverage percentages were fair: the difference between the expert annotations and model-predicted segmentation was less than 6–8%, which could be considered an encouraging result.

Analysis of SARS-CoV-2 transmission in airports based on real human close contact behaviors

The COVID-19 pandemic has significantly impacted people's daily lives for over three years. Airports, with their dense population and frequent close contact, pose a higher risk of respiratory infectious diseases compared to many other indoor environments. However, limited availability of data on close contact behavior has resulted in a gap in indoor exposure analysis. This study conducted depth sensor measurements and video data collection across nine areas of a northern (airport A) and a southern (airport B) airports in China by 11 participants. The data, comprising more than 44 h of close contact behaviors, including interpersonal distance, relative facial orientation, and the relative position of individuals, were analyzed using a semi-supervised machine learning method. Based on this analysis, a close contact transmission model for COVID-19 was developed, which considers the aforementioned close contact behaviors to assess the risk of exposure and the efficacy of interventions. The average close contact ratio in 9 airport's areas is 25.4 % (ranging from 6.1 % to 55.0 %), with passengers having the highest frequency of close contact in manual check-in areas. During close contacts, the average interpersonal distance in airports is 1.2 m (ranging from 1.1 to 1.4 m), being shortest in boarding areas. Face-to-face close contact is highest in charging areas, with a percentage of 46.9 %. If people maintain a distance of over 1.0 m in all areas, the total virus exposure could be reduced by 6.9 %–22.0 % compared to the actual situation. Dining areas have the highest virus exposure risk for both short-range inhalation and mucosal deposition, followed by manual check-in areas. This study provides a data support for the scientific epidemic prevention and control in airports from the viewpoint of close contact behaviors.

Investigating the Emotion Patterns of Students’ Abnormal Interactions in Primary Class Teaching Contexts

Students’ abnormal interactions pertain to the types of interactive behaviors that deviate from the prescribed learning contents and contravene the established learning objectives during classroom teaching. According to the emotion motivation theory, emotions experienced by students, which serve as a critical indicator of these abnormal interactions, assume a pivotal role in shaping the dynamics of learning activities. In primary school classrooms where frequent abnormal interactions occur, emotional exhaustion is highly prone to emergence and can further exacerbate students’ abnormal interactions, thereby significantly affecting the overall quality of classroom teaching. However, the relationship between emotions and students’ abnormal interactions remains limited and lacks a comprehensive understanding along with practical solutions. Hence, this study aims to investigate the emotion patterns of students’ abnormal interactions in primary class teaching contexts. By collecting video data from a sixth-grade classroom, we employ a combination of modified Flanders Interaction Analysis and intelligent technologies to explore students’ abnormal interactions and recognize emotion patterns of abnormal interactions. The findings of our study reveal that the classroom learning process engenders multiple types of students’ abnormal interactions, and different categories of students’ abnormal interactions were significantly different from their experienced emotions. In general, the sequence of chatting without permission followed by expressions of happiness and surprise emotions, and no response to the teacher’s questions followed by neutral and fearful expressions was remarkable during the classroom learning process. Building upon these findings, our study puts forth targeted recommendations for classroom teaching and management, with the ultimate goal of enhancing the quality of teaching and fostering the individual sustainable development of students.

Investigating Pedestrian Crossing Patterns at Crossing Locations Based on Trajectory Data Collected by UAV

Previous studies on pedestrian crossing have mostly focused on pedestrian crossing decisions; while as an important behavioral aspect, the pedestrian crossing process, i.e., their motions during the entire crossing process, has been narrowly studied. Understanding how pedestrian moves across the street during their entire crossing process helps identify risky movements and reasons for such movements, which can further help in the implementation of effective countermeasures. Therefore, this paper proposed a new and easily applied approach for investigating and understanding the pattern of the pedestrian crossing process at crosswalks based on vision-based trajectory tracking technology and UAV (unmanned aerial vehicle) data. This study uses UAV for collecting video data which is timesaving and has a sufficient coverage area, compared to other methods. For trajectory extraction, the vision-based Deep-SORT-Yolov5 architecture is applied. An improved DBSCAN (density-based spatial clustering of applications with noise) algorithm is introduced for clustering and identification of patterns of pedestrian crossing processes based on their trajectories. This approach is tested via a case study involving six marked crosswalks in Shanghai, China. By using the proposed method, different crossing patterns are extracted and compared. The results show reasonable outputs of trajectory patterns, which reasonably explain the potential instincts of the pedestrians and affecting factors on the behavior of the pedestrian crossing process. Suggestions are made based on the results. This paper contributes to a more comprehensive safety analysis of pedestrian crossings by considering the pedestrian crossing process. The model, along with the UAV-based trajectory observation method, provides an easily-applied and low-cost way of traffic data collection for the purpose of pedestrian safety evaluation.

A Real-Time Shipping Container Accident Inference System Monitoring the Alignment State of Shipping Containers in Edge Environments

Along with the recent development of artificial intelligence technology, convergence services that apply technology are undergoing active development in various industrial fields. In particular, artificial intelligence-based object recognition technologies are being widely applied to the development of intelligent analysis services based on image data and streaming video data. As such, in the port yard, these object recognition technologies are being used to develop port safety services in smart ports. Accidents are a frequent occurrence in port yards due to misaligned loading of ship containers. In order to prevent such accidents, various studies using artificial intelligence technology are underway. In this paper, we propose a real-time shipping container accident inference edge system that can analyze the ship container’s loading status from a safety point of view to prevent accidents in advance. The proposed system includes the collection of video data of the ship container, inferring the safety level of the alignment status of the ship container, and transmitting the inference results for the safety level. In this paper, the proposed inference model is implemented with YOLOv3, YOLOv4 and YOLOv7 networks and can be used in video monitoring to realize the accurate classification and positioning of three different safety levels (safe, caution, and danger) in real time. In the performance evaluation, the detection accuracy of the inference model implemented with the YOLOv4 network was greater than 0.95. Its performance was also significantly better than that of the inference model implemented with the YOLOv3 and YOLOv7 networks. Although it was slightly inferior to the YOLOv4 network in terms of the accuracy, the inference model implemented with the YOLOv3 network had a faster inference speed than the model implemented with the YOLOv4 and YOLOv7 networks. Because of the port safety scenario, in which the inference accuracy is more important than the inference speed, we applied the YOLOv4 algorithm to the inference model of the system.

Reliability and Accuracy of YouTube Peri-Implantitis Videos as an Educational Source for Patients in Population-Based Prevention Strategies.

Considering the need to improve patient knowledge, awareness, and compliance for peri-implantitis prevention, and patients' demand for better, quick, and convenient access to medical information, the present study primarily assessed the reliability and accuracy of YouTube videos on peri-implantitis and secondarily evaluated their educational value for the patients, and the related suitability, as part of population-based preventive strategies, to deliver valid information, potentially capable of improving patient knowledge and educational skills. This study's protocol was developed in advance, and computer history and cookies were cleared to avoid limitations based on preferred user histories. The search term "peri-implantitis" was defined using the Google Trends website, and videos were searched on YouTube on 5 December 2022. Video inclusion and data collection were conducted by independent pre-calibrated investigators. Descriptive statistics were performed on the videos' characteristics, source, category, target audience, popularity, source reliability, video information and quality (VIQI), content, and educational value. Pearson's correlation between educational value and all parameters was calculated. Videos with very low/low and medium/good/excellent educational value were compared using the Mann-Whitney U test. A total of 44 videos with medium values for popularity, VIQI, content, and educational value were analyzed. Most videos covered peri-implantitis treatment rather than etiology and prevention, about half were uploaded by dentists/specialists, and only 10% specifically targeted patients. Only 2% of YouTube videos about peri-implantitis had excellent educational value, and 5% had good educational value. Video educational value was correlated with VIQI and content beyond video length and source reliability. When comparing the very low/low and medium/good/excellent educational value YouTube videos on peri-implantitis, a significant difference was found in the Video Information and Quality Index, VIQI, and video content.

Bringing the Clinic Home: An At-Home Multi-Modal Data Collection Ecosystem to Support Adaptive Deep Brain Stimulation.

Adaptive deep brain stimulation (aDBS) shows promise for improving treatment for neurological disorders such as Parkinson's disease (PD). aDBS uses symptom-related biomarkers to adjust stimulation parameters in real-time to target symptoms more precisely. To enable these dynamic adjustments, parameters for an aDBS algorithm must be determined for each individual patient. This requires time-consuming manual tuning by clinical researchers, making it difficult to find an optimal configuration for a single patient or to scale to many patients. Furthermore, the long-term effectiveness of aDBS algorithms configured in-clinic while the patient is at home remains an open question. To implement this therapy at large scale, a methodology to automatically configure aDBS algorithm parameters while remotely monitoring therapy outcomes is needed. In this paper, we share a design for an at-home data collection platform to help the field address both issues. The platform is composed of an integrated hardware and software ecosystem that is open-source and allows for at-home collection of neural, inertial, and multi-camera video data. To ensure privacy for patient-identifiable data, the platform encrypts and transfers data through a virtual private network. The methods include time-aligning data streams and extracting pose estimates from video recordings. To demonstrate the use of this system, we deployed this platform to the home of an individual with PD and collected data during self-guided clinical tasks and periods of free behavior over the course of 1.5 years. Data were recorded at sub-therapeutic, therapeutic, and supra-therapeutic stimulation amplitudes to evaluate motor symptom severity under different therapeutic conditions. These time-aligned data show the platform is capable of synchronized at-home multi-modal data collection for therapeutic evaluation. This system architecture may be used to support automated aDBS research, to collect new datasets and to study the long-term effects of DBS therapy outside the clinic for those suffering from neurological disorders.

HPV-Related Oral Lesions: YouTube Videos Suitability for Preventive Interventions including Mass-Reach Health Communication and Promotion of HPV Vaccination.

Given the need to improve patient knowledge about HPV-related oral lesions, awareness of HPV infection prevention measures, and compliance with vaccination, as well as patient demand for free and easy access to well-tailored and time-saving health information, the present cross-sectional study examined the accuracy of relevant YouTube videos and their suitability for mass-reach health communication and HPV vaccination promotion. A video search was performed, using keywords obtained from the Google Trends website, until 9 January 2023. Video selection and data collection were performed by independent, pre-calibrated examiners. Descriptive statistics were performed on videos' general characteristics, source reliability, popularity, information and quality, content topics, vaccination-encouraging/discouraging messages, and educational value. Pearson's correlation was calculated between educational value and all parameters. Mann-Whitney U test compared very low/low vs. medium/good/excellent educational value and HPV vaccination-encouraging vs. -discouraging videos. Most of the 97 YouTube videos analyzed were moderately accurate and reliable, 53% had moderate/good/excellent educational value, and 80% encouraged HPV vaccination, making them suitable for mass-reach communication. The limited role of oral healthcare providers in uploading relevant content, with the poor dissemination of information about HPV-related benign and malignant oral lesions, may be expanded by purposefully using YouTube and other mass media to improve patient knowledge of HPV-related oral lesions and promote HPV vaccination, which also underscores its potential beneficial oral effects.

Investigating the differences in human behavior between conventional machining and CNC machining for future workforce development: A case study

Due to the high efficiency and consistent product quality, CNC machining has gained a dominant role in the modern machining industry. However, conventional machining still has its significance for certain production settings, e.g. prototyping, and machining workforce preparation. CNC and conventional machining require particular skills and knowledge, which can be unique to specific types of machine tools or overlap to a certain extent. With increasing production volume demands and an aging workforce, a need for efficient quantification, preservation and transfer of said skillsets arises to ensure effective preparation of future professionals and the undisrupted operation of the manufacturing industry. Moreover, the observed shift towards human-centered manufacturing systems in the Industry 4.0 necessitates obtaining an in-depth understanding of human roles in machining. The following paper proposes a novel research approach based on collection and analysis of eye-tracking and video data supplemented by verbal interviews, surveys and self-assessment. A conducted case study spans the entirety of the machining process, from part evaluation, cutting strategy determination, machining operations, to process re-evaluation and optimization. The results show that far greater variability in cutting strategy in terms of operation order, number of operations and used production tooling between consecutive production runs can be observed for conventional machining, with little variation in those terms noted for CNC-based production. Overall, the collected data has allowed to gain an insight into the machinist's decision-making processes and the rationale for observed cutting strategy changes, allowing for potential future application of the proposed research method in improving the machining training and potentially aiding process design by applying the outcomes of studies performed with the use of presented research method to expert systems and future CAM/CAE software solutions.

Behavior-Based Video Summarization System for Dog Health and Welfare Monitoring

The popularity of dogs has been increasing owing to factors such as the physical and mental health benefits associated with raising them. While owners care about their dogs' health and welfare, it is difficult for them to assess these, and frequent veterinary checkups represent a growing financial burden. In this study, we propose a behavior-based video summarization and visualization system for monitoring a dog's behavioral patterns to help assess its health and welfare. The system proceeds in four modules: (1) a video data collection and preprocessing module; (2) an object detection-based module for retrieving image sequences where the dog is alone and cropping them to reduce background noise; (3) a dog behavior recognition module using two-stream EfficientNetV2 to extract appearance and motion features from the cropped images and their respective optical flow, followed by a long short-term memory (LSTM) model to recognize the dog's behaviors; and (4) a summarization and visualization module to provide effective visual summaries of the dog's location and behavior information to help assess and understand its health and welfare. The experimental results show that the system achieved an average F1 score of 0.955 for behavior recognition, with an execution time allowing real-time processing, while the summarization and visualization results demonstrate how the system can help owners assess and understand their dog's health and welfare.

A multimodal corpus to study videoconference interactions for techno-pedagogical competence in second language acquisition and teacher education

This article aims to describe the construction and annotation of a multimodal and multilingual (French, English, and Mandarin Chinese) corpus for the study of second language acquisition and professional development in language teaching. The corpus was built within a research project whose main objective is to determine which techno-semio-pedagogical competencies are developed informally and which ones require formal training. In this paper, we explain the theoretical framework adopted, characterized by an ecological approach to the interactive environment. We subsequently illustrate the procedures for the collection of audio, video and eye-tracking data. We also provide details of the annotation of raw data to produce a corpus of analysis drawing on different automatic and semi-automatic annotation tools. Finally, we explain how such a corpus enables us to study the development of learners’ multimodal competence and tutors’ techno-pedagogical competence and point to some other possible research questions.

Adaptive Weighted Flow Net Algorithm for Human Activity Recognition Using Depth Learned Features

Human Activity Recognition (HAR) from video data collections is the core application in vision tasks and has a variety of utilizations including object detection applications, video-based behavior monitoring, video classification, and indexing, patient monitoring, robotics, and behavior analysis. Although many techniques are available for HAR in video analysis tasks, most of them are not focusing on behavioral analysis. Hence, a new HAR system analysis the behavioral activity of a person based on the deep learning approach proposed in this work. The most essential aim of this work is to recognize the complex activities that are useful in many tasks that are based on object detection, modelling of individual frame characteristics, and communication among them. Moreover, this work focuses on finding out the human actions from various video resolutions, invariant human poses, and nearness of multi objects. First, we identify the key and essential frames of each activity using histogram differences. Secondly, Discrete Wavelet Transform (DWT) is used in this system to extract coefficients from the sequence of key-frames where the activity is localized in space. Finally, an Adaptive Weighted Flow Net (AWFN) algorithm is proposed in this work for effective video activity recognition. Moreover, the proposed algorithm has been evaluated by comparing it with the existing Visual Geometry Group (VGG-16) convolution neural networks for making performance comparisons. This work focuses on competent deep learning-based feature extraction to discriminate the activities for performing the classification accuracy. The proposed model has been evaluated with VGG-16 using a combination of regular UCF-101 activity datasets and also in very challenging Low-quality videos such as HMDB51. From these investigations, it is proved that the proposed AWFN approach gives higher detection accuracy of 96%. It is approximately 0.3% to 7.88% of higher accuracy than state-of-art methods.

비디오게임의 감정조절이 사용자의 지속적 이용의도에 미치는 융합적 영향에 관한 연구 - 공감과 몰입의 관점을 기반으로

This paper investigates game players" willingness to use games during the COVID-19 period and finds that after the epidemic, the game industry"s development will face the challenge of losing game users. Therefore, how to improve users" willingness to continue using games and maintain the scale of game users will be an urgent problem to be solved for current scholars. Because users" continuance intention has a direct predictive effect on users" continuous use behavior, this paper will aim to improve users" continuance intention and prevent the shrinking of game users after the end of the epidemic as the research purpose. This article will start from the new perspectives of player empathy flow and investigate the influence of emotional regulation on users" continuance intention in video games and the mechanism of action between them. Through the survey of current video users, data collection, and analysis, the following conclusions are drawn: (1) In video games, emotional regulation is significantly correlated with flow, empathy, and users" continuance intention. (2) Emotion regulation has a positive effect on flow, empathy. (3) flow and empathy have positive effects on users" continuance intention in video games. (4) In video games, flow mediates the process of emotional regulation on users" continuance intention Empathy has no significant mediating effect in the process of emotional regulation on users" continuance intention. Based on conclusions (1) and (2), this paper suggests that game designers and scholars can start with empathy and flow to study ways to improve players’ emotional regulation. Based on the conclusion (3), this paper suggests that game designers can improve users" continuance intention from the aspects of flow and empathy. Based on the conclusion (4), that is, flow is the mechanism by which emotional regulation affects users’ continued intentions, this paper suggests that designers start with the mediating role of flow to study the impact of emotional regulation on users" continuance intention. The research content of this paper increases the theoretical research related to emotion regulation, empathy, and flow users" continuance intention in game theory, and provides new method suggestions for maintaining the existing scale of video game users.

Low-cost framework for 3D reconstruction and track detection of the railway network using video data

3D reconstruction from video data is proposed for mapping the Egyptian railway network. A generic framework is proposed and applied based on video data. This framework mainly passes through three main stages. The pre-processing stage contains system implementation, data acquisition, the keyframes extraction, and camera calibration. The second stage is the generation of the 3D reconstruction models from a set of two-dimensional images. This stage is mainly based on the feature correspondence process and Structure from Motion (SfM) techniques. The 3D coordinates of the feature points and relative camera motion parameters are refined using the bundle adjustment algorithms to generate an accurate 3D reconstruction model. Many models are reconstructed based on different parameters/scenarios to evaluate the most accurate model. The best 3D model was selected and used to generate an orthogonal photo. Finally, the third stage is the track detection framework which uses the generated orthogonal image for automatic detection of railway tracks. The framework is tested on a dataset of a railway network with an approximate length of 20 km. The accuracy of the results is compared with field survey data conducted in the same area using conventional surveying instruments. From the results, the recommended train speed for reasonable video data collection is 10 kph with an overlap percentage of 95 %. The RMSE of the extracted track width is 0.03 m, and it contributes to the efficiency of our approach in the reconstruction of 3D surfaces compared to the recommended procedures in traditional techniques.