Jose Luis Casado Rico , Israel Villarrasa-Sapiña , Xavier García-Massó , Gonzalo Monfort-Torres
{"title":"Differences in hand acceleration and digital reaction time between different skill levels of Counter Strike players","authors":"Jose Luis Casado Rico , Israel Villarrasa-Sapiña , Xavier García-Massó , Gonzalo Monfort-Torres","doi":"10.1016/j.entcom.2024.100797","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100797","url":null,"abstract":"<div><p>Due to the international prominence that eSports are acquiring, it is necessary to study their effects on human behavior. Specifically, the difference between the motor skills of players with different levels of experience. Therefore, the main objective of this work is to observe whether there are differences in hand accelerations (mouse handling) and digital reaction time between different skill levels. 21 subjects participated and were grouped according to their skill level (Low, Intermediate and Elite levels). The results showed greater hand accelerations in the Elite and Intermediate Skills players than Low-Skill players (p < 0.05). The hand-eye reaction time was lower in the Elite-Skill group than in the Low-Skill group (p = 0.008). Those with a Low-Skill had a higher dots per inch (DPI) than those with Intermediate and Elite Skills (p < 0.05). Elite-Skill players used larger screens than Low-Skill players (p = 0.034). The results suggest that players with better skills have better hand acceleration and shorter hand-eye reaction time.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1875952124001654/pdfft?md5=1ff432bcb5e50f33f1d3489ff9e8a0fc&pid=1-s2.0-S1875952124001654-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Immersive e-learning application in intelligent teaching of English composition based on neural network algorithm","authors":"Bing Zhao , Deng Pan","doi":"10.1016/j.entcom.2024.100710","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100710","url":null,"abstract":"<div><p>Technical solutions for evaluating traditional English teaching composition and other problems, such as excessive subjectivity, waste of time, slow feedback, short scoring time, heavy tasks and so on. This paper first analyzes the theoretical basis of neural network algorithm, and completes the preprocessing of the composition text submitted by students from three aspects: text segmentation, text representation and text fragment retrieval. Then, an intelligent evaluation system of English composition is established, and the scoring standard of English composition is determined. On this basis, the design of English scoring module is improved, and an anti cheating module for common plagiarism problems in English composition is added. Finally, the English composition intelligent evaluation system is tested. It can effectively complete the target task, evaluate and correct the composition, and has a higher recognition success rate and accuracy rate. By studying the algorithm and applying it to the construction of English composition intelligent evaluation system, this paper successfully designs a kind of English composition intelligent evaluation system, which promotes the development of teaching work.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Influence of social robots and digital media based on edge computing on dance performance entertainment environment","authors":"Li Pei","doi":"10.1016/j.entcom.2024.100781","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100781","url":null,"abstract":"<div><p>This article proposes a new social robot system that achieves robot dance creation through dance image feature extraction and dance action imitation, thereby promoting the development of digital media dance entertainment culture. This paper uses the edge computing model, and on the basis of the robot’s own computing power, puts some computing tasks related to image processing and action imitation down to the edge of the network to improve the real-time and fluency of dance performance. The study utilized image feature extraction technology to analyze and process dance images, achieving the imitation of dance movements by robots. By imitating and innovating dance movements through robots, unique forms and content can be provided for digital media dance performances. Digital media technology provides rich means and forms of expression, which can make robot dance performances more creative and artistic.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Using provenance and replay for qualitative analysis of gameplay sessions","authors":"Leonardo Thurler, Sidney Melo, Leonardo Murta, Troy Kohwalter, Esteban Clua","doi":"10.1016/j.entcom.2024.100778","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100778","url":null,"abstract":"<div><p>There is an increasing interest to use game telemetry for analyzing gameplay sessions, with numerous techniques created to help game developers analyze different game aspects like game balancing and behavioral analysis. Among different gameplay session analysis techniques, the collection of provenance data has stood out due to a crucial advantage of this approach: the possibility to identify cause–effect relationships between game events. In previous work, we presented our conceptual framework called Prov-Replay, which provides a replay synchronized with an interactive provenance graph visualization. We validate Prov-Replay by creating PinGU Replay, a tool that implements our conceptual framework and applied it in a commercial game. Due to the promising results, this paper extends our previous work by presenting a detailed overview about Prov-Replay implementation, introducing a new feature that provides an analysis dashboard, and applying our experiment methodology to a new commercial game. We also enhance the concept of analytics related to provenance through the replay pipeline. Finally, we made PinGU Replay available as open-source software. Our new experiment results reinforce that, when using our conceptual framework fundamentals, it is possible to improve the efficiency and effectiveness of qualitative analysis process.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Entertainment robots based on digital new media application in real-time error correction mode for Chinese English translation","authors":"Yanmei Geng","doi":"10.1016/j.entcom.2024.100789","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100789","url":null,"abstract":"<div><p>With the assistance of digital new media technology, virtual entertainment robots, as a new learning experience mode, can effectively enhance the interactive process of e-learning learning. This article studies the application of entertainment robots based on digital new media in real-time error correction mode for Chinese English translation. Through experiments, it has been verified that the flexible use of deep learning technology can significantly improve user satisfaction and translation accuracy, and has already improved the level of error correction and positioning. This article first introduces the existing mainstream machine learning models, including supervised neural network models and attention mechanisms. On this basis, the system was optimized to further improve its performance. At the same time, this article proposes new improvement plans to address the shortcomings of current mainstream translation systems. We conducted comparative experiments on the error correction model of the proposed adaptive algorithm for specific error types, and also tested it using real datasets. Research has shown that using adaptive algorithms based on reinforcement deep learning can not only significantly optimize the error correction efficiency of our system, but also flexibly adapt to the needs of various optimization strategies.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial intelligence based social robots in the process of student mental health diagnosis","authors":"Jinyi Zhang, Tianchen Chen","doi":"10.1016/j.entcom.2024.100799","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100799","url":null,"abstract":"<div><p>This paper in order to achieve the application of artificial intelligence based social robots in the process of student mental health diagnosis. When designing the architecture of social robots, factors such as interactivity, adaptability, and scalability were taken into consideration to ensure that they possess human like interaction characteristics and flexibility. Subsequently, a model was constructed based on deep learning technology to achieve functions such as sentiment classification, text mining, and optimization strategies. The input data set of the training model comes from the user’s interaction records and behavior data on the Internet social platform, as well as the user’s feedback information in the process of using the robot. The research on psychological data classification has constructed corresponding algorithms based on pointer networks and text models to achieve text feature extraction and classification. The psychological emotion mining module extracts emotional states from user discourse and maps them to corresponding categories of psychological problems. Finally, based on the user input question content, classify and optimize psychological problems. Research has shown that the robot has certain accuracy and practicality in data classification and student mental health diagnosis.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of social entertainment robots based on machine learning algorithms and the Internet of Things in collaborative art performances","authors":"Zhao Zhenhua , Guo Feng","doi":"10.1016/j.entcom.2024.100784","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100784","url":null,"abstract":"<div><p>In terms of control strategies for social entertainment robots, advanced control system design was adopted in the study, aiming to enable robots to achieve efficient collaborative art performances. The control system is based on machine learning algorithms and Internet of Things technology, combined with the application of sensing technology, providing accurate environmental perception and real-time feedback mechanisms for robots. Considering the collaboration and interaction between robots and human actors, control strategies adapted to different scenes were designed by analyzing and understanding the needs of artistic performances. These strategies not only consider the robot’s own actions and performance, but also the interaction with human actors and the coordination with the entire performance scene. The control method in this article combines machine learning algorithms and sensing technology to enable robots to make intelligent decisions and action planning by learning and perceiving real-time environmental information. By modeling and simulating the structure and characteristics of the robot, precise planning and control of the robot’s motion trajectory can be achieved. Through dynamic modeling, it is possible to better understand the motion characteristics and energy consumption of robots, and to adjust and optimize their actions during the performance process.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Application of entertainment e-learning mode based on genetic algorithm and facial emotion recognition in environmental art and design courses","authors":"Shanshan Li","doi":"10.1016/j.entcom.2024.100798","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100798","url":null,"abstract":"<div><p>This article is based on facial recognition and further designs an entertainment electronic learning mode. Facial emotion recognition may be affected by light, angle, and expression. In order to improve recognition accuracy and stability, distortion adjustment techniques were used to process facial images, ensuring that the model can accurately capture and recognize the features of facial expressions. The study applies online facial emotion recognition to entertainment electronic learning modes, where learners interact with the system. The system can detect and recognize learners’ facial expressions in real-time, and provide corresponding feedback and learning resources based on different expressions. By collecting and analyzing experimental data, evaluate the practicality of the model and the level of acceptance and satisfaction of learners towards the model. The entertainment electronic learning mode based on facial recognition provides an innovative learning approach by constructing a pattern architecture, distortion adjustment, and applying online facial emotion recognition. By optimizing the positioning and integrating the entertainment electronic learning mode with environmental art and design courses, we aim to enhance students’ learning motivation and interest. Develop optimization strategies to enhance students’ comprehensive abilities in the field of environmental art and design.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141487254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Entertainment robot based on IoT and VR interaction for motion training posture monitoring","authors":"Shutao Tong , Weiguo Liao","doi":"10.1016/j.entcom.2024.100788","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100788","url":null,"abstract":"<div><p>With the development of artificial intelligence technology, the role of social robots in sports activities has gradually become prominent. Robots can help with intelligent control of sports venues, assist in motion guidance, and engage in emotional communication with users, helping athletes adjust their emotions. This article studies the motion training posture monitoring of entertainment robots based on the interaction between the Internet of Things and VR. A new high-intensity motion attitude monitoring system based on quantum dot photodetectors has been developed to improve monitoring accuracy and stability. Entertainment robots can simulate and analyze motion training postures, and provide relevant technical guidance. The quantum dot photodetectors were integrated into the sensor components, and the collected data was transmitted to the data acquisition and processing unit through signal transmission and processing modules. The collected data was processed and analyzed to achieve real-time monitoring and tracking of the target object’s attitude. Multiple experiments were conducted on the entire system, simulating various high-intensity exercise environments, and evaluating the performance parameters of the system. Through continuous optimization and improvement, the stability and accuracy of the system were ultimately ensured.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Digital media entertainment technology based on artificial intelligence robot in art teaching simulation","authors":"Xiayan Liao, Peng Cao","doi":"10.1016/j.entcom.2024.100792","DOIUrl":"https://doi.org/10.1016/j.entcom.2024.100792","url":null,"abstract":"<div><p>The combination of digital media entertainment technology and artificial intelligence robots provides new possibilities for art teaching, providing students with a richer and more personalized learning experience. The aim of this study is to explore the application of artificial intelligence based digital media entertainment technology in art teaching simulation. By designing an intelligent digital media system, the interactivity, personalization, and effectiveness of art teaching can be improved. Research the use of digital media interactive technology to create an immersive art learning environment. Applying artificial intelligence recommendation algorithms to recommend personalized art teaching resources to students based on their learning history, interests, and abilities, in order to improve learning efficiency and outcomes. Combining artificial intelligence recommendation algorithms, intelligently recommend suitable art teaching resources for different students’ needs and levels. Through teaching interaction simulation testing, evaluate the interaction effect and user experience of the system in simulated art teaching scenarios, identify and fix potential problems. By utilizing digital media interaction technology and personalized recommendation algorithms, designing an intelligent digital media system can effectively enhance the interactivity, personalization, and effectiveness of art teaching, providing students with a better learning experience.</p></div>","PeriodicalId":55997,"journal":{"name":"Entertainment Computing","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141482938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}